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Open Access Article
1 - A method based on deep neural network optimized with Huffman algorithm and meta-heuristic algorithms for medical image compression and reconstruction
Mohammad Hossein Khalifeh Mehdi Taghizadeh Mohammad Mehdi Ghanbarian جاسم جمالیThis research makes use of two different approaches to compress medical images for long-term purposes. In the first method, images are compressed using the Huffman cipher and then simplified using a hierarchical modeling based on a neural network-designed categorization MoreThis research makes use of two different approaches to compress medical images for long-term purposes. In the first method, images are compressed using the Huffman cipher and then simplified using a hierarchical modeling based on a neural network-designed categorization. A prediction strategy based on deep neural network training is employed in the second method. This technique uses a trained neural network to infer the locations of individual pixels, hence reducing the amount of data required to describe a picture. Huffman compression encryption is used on the leftover data. An enhanced spatial filtering technique is used to decode the picture data, and the wild horse optimization and gray wolf optimization meta-heuristic algorithms are then used to produce a rebuilt image. Without compromising compression efficiency, this allows for a more realistic application of the suggested solutions in non-deterministic contexts. The suggested approaches allow for picture simplification, which has resulted in faster decoding. Structural similarity index modulation, time and peak signal-to-noise ratio have been improved by an average of 2, 30.1 and 15.15%, respectively. The suggested algorithms were able to compress medical photos with very high quality level, as compared to the current deep learning-based methods. Manuscript profile -
Open Access Article
2 - An Improved Imperialist Competitive Algorithm based on a new assimilation strategy
Seyed Mojtaba Saif -
Open Access Article
3 - Combining Harmony search algorithm and Ant Colony Optimization algorithm to increase the lifetime of Wireless Sensor Networks
Zahra Kamaei Hamidreza Bakhshi Behrooz Masoumi -
Open Access Article
4 - Robot Path Planning Using Cellular Automata and Genetic Algorithm
Zeynab Sedreh Mehdi Sadeghzadeh -
Open Access Article
5 - An Improved Bat Algorithm based on Whale Optimization Algorithm for Data Clustering
Neda Damya Farhad Soleimanian Gharehchopogh -
Open Access Article
6 - An Optimization K-Modes Clustering Algorithm with Elephant Herding Optimization Algorithm for Crime Clustering
Farhad Soleimanian Gharehchopogh Sevda Haggi -
Open Access Article
7 - An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms
Mohammad Hassanzadeh farshid keynia -
Open Access Article
8 - A modified differential evolution algorithm with a balanced performance for Exploration and Exploitation phases
Iraj Naruei farshid keynia -
Open Access Article
9 - An optimal VM Placement in Cloud Data Centers Based on Discrete Chaotic Whale Optimization Algorithm
mohammad masdari sasan Gharehpasha ahmad jafarian -
Open Access Article
10 - Energy-aware and Reliable Service Placement of IoT applications on Fog Computing Platforms by Utilizing Whale Optimization Algorithm
Yaser Ramzanpoor Mirsaeid Hosseini Shirvani Mehdi GolSorkhTabar -
Open Access Article
11 - An Optimization-based Learning Black Widow Optimization Algorithm for Text Psychology
Ali Hosseinalipour Farhad Soleimanian Gharehchopogh mohammad masdari ALi Khademi -
Open Access Article
12 - Solving random inverse heat conduction problems using PSO and genetic algorithms
I. Hossein Zade Shahbolaghi R. Pourgholi H. Dana Mazraeh S.H. TabasiThe main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solvin MoreThe main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorithm and the genetic algorithm, we solve them. The algorithms presented in this article have advantages over other old methods that have been presented so far. Implementing these algorithms is simpler, have less run time and produce better approximation. The numerical results obtained in this paper also show that the solutions obtained for the examples presented in the numerical results section are highly accurate and have less error. All of the algorithms in this paper to obtain the desired numeric results, have been implemented on the Pentium (R) Dual core E5700 processor at 3.00 GHz. Manuscript profile -
Open Access Article
13 - Modeling and Comparison of Fuzzy and Non-Fuzzy Multi-Objective Evolution Optimization Portfolios in Tehran Stock Exchange
Mohammad Fallah Hadi Khajezadeh Dezfuli Hamed NozariSelecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real wor MoreSelecting the optimal stock portfolio is one of the most important issues in the field of financial research, which tries to choose the optimal combination of assets in order to create maximum utility for the investor, Given that the return on securities in the real world is often vague and inaccurate, one of the most important investment challenges is uncertainty about the future. In this paper the problem of selecting and optimizing securities portfolios with different modeling goals has been solved and compared. The designed models have considered both the nature of the portfolio selection issue and the considerations considered by the shareholder in the portfolio selection. The uncertainty quality of the future return of a given portfolio is estimated using fuzzy LR numbers, while its return torques are measured using possibility theory. The most important purpose of this paper is to solve the problem and compare portfolio selection models with simultaneous optimization of two, three, and four objectives. For this purpose, the NSGA-II genetic algorithm is used and the mutation and intersection operators are designed specifically to generate possible solutions to the cardinality constraint of the problem. Finally, the efficiency and performance of the models in case of using fuzzy logic and not using it have been compared and it has been determined that the use of fuzzy logic and possibility theory leads to the formation of portfolios with higher performance and higher efficiency. Manuscript profile -
Open Access Article
14 - Modeling The Behavior of Concrete Dams using Artificial Neural Network and Logistic Regression Methods
Fardin Saeid Mohsen Irandoust Navid JalalkamaliBackground and Aim: Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the ne MoreBackground and Aim: Dam measurement and behavior assessment is a new issue that can be due to changes in available parameters to develop a model examining the behavior of individual parameters on the dam as well as on each other and analyze the changes and create the necessary policies. This study aims to propose a hybrid method involving logistic regression with particle swarm optimization algorithm with real value to predict the behavior of dam equipment.Method: In this study, from 365 days data, from 04/20/2018 to 04/20/2019, of which 600 sets of dam equipment data including parameters of water temperature, water level, valve pressure, sedimentation rate, pore pressure, air temperature, inlet water volume, specific dam characteristics, concrete conditions, reservoir water level, horizontal and vertical displacement, transmission connection components and ground acceleration, strength, pressure, tensile and high stress were used for modeling. Real value-logistic regression and 120 datasets were used for modeling the should be added of particle group optimization algorithm. To evaluate the performance of the proposed method, four statistics including coefficient of determination (R2), root mean square error (RMSE), scattering coefficient (SI), and means bias error (MBE) were used.Findings: The results showed that the model has an acceptable performance in predicting piezometric pressure in the dam body. Also, the results of the artificial neural network model show acceptable convergence with R2 = 0.930 and SSI = 8.587. The results related to the training data of the model also indicate that the mean (µ) and standard deviation (σ) of the proposed model are equal to 1.341 and 1.526 for the training data and these values for the validation data are equal to 1.576 and 2.247, respectively indicating the good performance of the proposed model. In the cumulative probability criterion, the proposed model with P50 = 0.940 and P90 = 1.742 indicates that the results are acceptable.Results: The results indicate that the real value-logistic regression particle swarm optimization implements the principle of structural risk reduction instead of minimizing the experimental risk that provides excellent generalization for small sample sizes. The ratio of predicted piezometric values to read values for about 72% of the data in this model is about one, indicating the appropriate training and predictive power of this model. Finally, according to the evaluation criteria, the hybrid model performs better than the presented methods. Manuscript profile -
Open Access Article
15 - Optimization and Prediction Changes of Groundwater Quality Parameters Using ANN+PSO and ANN+P-PSO Models (Case Study: Dezful Plain)
Fahimeh Sayadi Shahraki Abdolrahim hooshmand Atefeh Sayadi ShahrakiBackground and Objective: One of the main aims of water resource planners and managers is the estimation and prediction of groundwater quality parameters to make managerial decisions. In this regard, many models have been developed which proposed better managements in o MoreBackground and Objective: One of the main aims of water resource planners and managers is the estimation and prediction of groundwater quality parameters to make managerial decisions. In this regard, many models have been developed which proposed better managements in order to maintain water quality. Most of these models require input parameters which are hardly available or their measurements are time consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by human's brain are a better choice.Method: The present study stimulated the groundwater quality parameters of Dezful plain including Sodium Adsorption Ratio (SAR), Electrical Conductivity (EC), Total Dissolved Solids (TDS), using ANN+PSO and ANN+P-PSO models and in the end is comparing their results with measured data. The input data for TDS quality parameter consist of EC, SAR, pH, SO4, Ca, Mg and Na, for SAR including the TDS, pH, Na, Hco3 and quality parameter of EC contains So4, Ca, Mg, SAR and pH, gathered from 2011 to 2015.Findings: The results indicated that the highest prediction accuracy of quality parameters of SAR, EC and TDS is related to the ANN+P-PSO model so that the MAE and RMSE statistics have the minimum and has the maximum value for the model. The results showed that RMSE for PSO in predicting SAR, EC and TDS were 0.09, 0.045 (µs/cm) and 0.053 (mg/l) in testing period, respectively. These statistical criteria were 0.039, 0.031 (µs/cm) and 0.045 (mg/l) for P-PSO in this period, respectively.Discussion and Conclusion: The results showed that P-PSO had more accuracy compared to PSO. In addition, there were no significant differences between ANN and collecting values. So, it is recommended that ANN were applied to determine nitrate concentration in groundwater. Manuscript profile -
Open Access Article
16 - Optimization the Availability of a System with Short Circuit and Common Cause Failures
مانی شریفی محمدرضا شهریاری شاهین خوش نیت Redundancy allocation problem is one of the most important problems in reliability field. In this problem, the reliability and availability of the systems are maximized via allocating redundant components to subsystems. Many different assumptions are considered to More Redundancy allocation problem is one of the most important problems in reliability field. In this problem, the reliability and availability of the systems are maximized via allocating redundant components to subsystems. Many different assumptions are considered to draw this problem near to real conditions. In this paper, we work on a system with k-out-o-n subsystems as well as considering short circuit and common cause failures for the components in each subs in addition to ordinary components failures. Obviously, the components are repairable. We present a Markov model to show the effects of these two failures on system availability. For solving the presented model, we used Biographic Based Optimization (BBO) algorithm and minimize the system cost to achieve the predetermined system availability. We used the BBO algorithm for calculating the availability of the system, and response surface methodology for tuning the algorithm parameters. Manuscript profile -
Open Access Article
17 - Lorenz hyper chaotic system parameter estimation using improved whale optimization algorithm with Tabu Search
Mahsa Esmaeilnia Mostafa Saadatifar Mahdi YaghoobiLorenz hyper chaotic system parameter estimation using improved whale optimization algorithm with Tabu Search Chaotic systems are very complex dynamic systems that have some special characteristics such as high sensitivity to initial conditions, lack of statistical pre MoreLorenz hyper chaotic system parameter estimation using improved whale optimization algorithm with Tabu Search Chaotic systems are very complex dynamic systems that have some special characteristics such as high sensitivity to initial conditions, lack of statistical prediction, and despite seemingly random behavior, chaotic systems are completely deterministic. Estimation of parameters of super-chaotic oscillators is one of the most important issues in the field of chaos. Parameter estimation of hyper-chaotic systems can be considered as a multivariate optimization problem. This article aims to present a new method for estimating the parameters of the superchaotic Lorenz system based on the improvement of the whale algorithm with the forbidden search algorithm. The simulation results show that the whale algorithm has a high competitive power compared to similar meta-heuristic algorithms. Estimation of parameters of super-chaotic oscillators is one of the most important issues in the field of chaos. Parameter estimation of hyper-chaotic systems can be considered as a multivariate optimization problem. This article aims to present a new method for estimating the parameters of the superchaotic Lorenz system based on the improvement of the whale algorithm with the forbidden search algorithm. The simulation results show that the whale algorithm has a high competitive power compared to similar meta-heuristic algorithms. Manuscript profile -
Open Access Article
18 - Design of Optimal Sugeno-type fuzzy Controller for Speed Control of DC Motor Including Drive and Chopper Dynamic Considering Multi-Objective Optimization Using Teaching Learning Optimization Algorithm
ali sedaratnia majid moradi zirkohi najmeh cheraghi shiraziDue to the simple structure of DC motors, these motors have found many applications in industry.Therefore, in this paper, the speed control of DC motor is investigated by considering the dynamics of drive and chopper with Sugeno-type fuzzy controller. A chopper is used MoreDue to the simple structure of DC motors, these motors have found many applications in industry.Therefore, in this paper, the speed control of DC motor is investigated by considering the dynamics of drive and chopper with Sugeno-type fuzzy controller. A chopper is used to control the voltage applied to the DC motor armature. Considering the dynamics of the chopper drive increases the complexity of the system. After designing the fuzzy controller to increase the performance of the control system, the fuzzy controller parameters are adjusted using a teaching-learning-based optimization algorithm. This algorithm is new and one of its features is its small number of parameters. The results show that the fuzzy controller has better performance against changes in system parameters and uncertainties compared to the classic PID controller. Considering the appropriate criterion function, the value of the cost function for the proposed method is 0.2. But with the optimized PID controller about 0.31 which shows a 55% superiority of the proposed method. Manuscript profile -
Open Access Article
19 - Designing Optimal Neural Networks Controller to Regulate and Control the Output Voltage of DC-DC Boost Converters
Mohammad Zaraei Majid Moradi Zirkohi Najmeh Cheraghi ShiraziDue to the many applications of DC to DC converters in electronics, regulating their output voltage is very important. In many applications it is necessary to change the DC voltage from one level to another. DC -DC converters are used for this purpose. The conversion of MoreDue to the many applications of DC to DC converters in electronics, regulating their output voltage is very important. In many applications it is necessary to change the DC voltage from one level to another. DC -DC converters are used for this purpose. The conversion of DC voltage from one level to another is done by switching elements such as transistors and diodes. Recently, the control of these converters has found a special place in scientific texts. Therefore, one of the objectives of this paper is to control and regulate the output voltage of the converter. The controller proposed in this paper to control the DC voltage level of the converter output is an optimized neural network controller with an algorithm based on colonial competition. The proposed controller function is that first the neural network is designed according to the expected goals of the system and then it is optimized by determining a suitable multi-objective benchmark function using the network structure optimization algorithm. This improves the performance of the control system. Because the proper selection of design parameters has a great role in the performance of the neural network that plays the role of controller. The proposed neural network function is to apply the appropriate signal transducer (PWM signal) to the switching elements in order to increase the performance. The results compared to the PID controller indicate the superiority of the proposed method. Manuscript profile -
Open Access Article
20 - Design of Anomaly Based Intrusion Detection System Using Support Vector Machine and Grasshopper Optimization Algorithm in IoT
Sepehr Sharifi Soulmaz GheisariComputer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, networ MoreComputer networks play an important and practical role in communication and data exchange, and they also share resources with complete ease. Today, various types of computer networks have emerged, one of which is the Internet of Things. In the Internet of Things, network nodes can be smart objects, and in this sense, this network has many nodes and there is a lot of traffic in this network. Like any computer network, it faces its own challenges and problems, one of which is the issue of network intrusion and disruption. This dissertation focuses on detecting anomaly-based intrusion into the Internet of Things using data mining. In this study, after collecting and preparing data, the improved support vector machine with grasshopper optimization algorithm is used as a proposed method to detect anomaly-based intrusion in the Internet of Things. The bagging and k-nearest neighbor classifiers and Basic SVM are compared based on error types and standard performance criteria. The simulation results show 97.2% accuracy in the proposed method and better performance compared to other methods. Manuscript profile -
Open Access Article
21 - Reduce spike noise from artificial aperture radar (SAR) images using Corvette conversion
Ameneh Rajabpour boshehri Ahmad KeshavarzIn this paper, an adaptive method based on carroll conversion is introduced to reduce spike noise. Speckle noise is a multiplicative impurity that in this paper we first convert to mass with a preprocessing step. An interest function is then introduced to threshold the MoreIn this paper, an adaptive method based on carroll conversion is introduced to reduce spike noise. Speckle noise is a multiplicative impurity that in this paper we first convert to mass with a preprocessing step. An interest function is then introduced to threshold the Coralt coefficients, which has three general thresholds. An objective function, based on an estimated noise correlation with the edges of the output image, then provides the optimal parameters in the threshold of the interest function by searching with a general search algorithm called the particle swarm optimization algorithm. The appropriate objective function is then considered for the PSO algorithm search and the results of speckle reduction are measured by the Violet method. Manuscript profile -
Open Access Article
22 - Introducing a new query database optimization method
Peyman Arebi Amir Masoud Bidgoli Serajodin KatebiThe grid database tries to store and scatter data over a wide geographical area in order to create a structure for storing data across the lattice environment in a distributed and heterogeneous manner. Due to the large amount of data, transaction processing in such an e MoreThe grid database tries to store and scatter data over a wide geographical area in order to create a structure for storing data across the lattice environment in a distributed and heterogeneous manner. Due to the large amount of data, transaction processing in such an environment is very complex and time consuming. Obviously, using queries without optimization will greatly reduce the efficiency of transactions in this database, while using appropriate optimization algorithms can greatly increase efficiency. Many algorithms have been proposed to optimize queries, but due to the different network environments, different optimization algorithms are needed. This paper presents an algorithm that is consistent with the structure of lattice computing and works well in lattice database systems with high data volumes. Manuscript profile -
Open Access Article
23 - Optimal Routing of Rocket Motion using Genetic Algorithm and Particle Swarm Optimization
Reza Tarighi M.H. Kazemi mohammad hosein khalesi -
Open Access Article
24 - FA-ABC: A Novel Combination of Firefly Optimization Algorithm and Artificial Bee Colony for Mathematical Test Functions and Real-World Problems
Ali reza Shafiee sarvestany Mohammadjavad Mahmoodabadi -
Open Access Article
25 - Investigating the Effect of Process Parameters on Reducing the Peeling Stress in Adhesive Joints of Composite Materials
Saeed Yaghoubi Mohammad Shishehsaz Kiamehr Rouzbakhshzadeh -
Open Access Article
26 - Transmission Expansion Planning Using Bacterial Foraging Optimization Algorithm
Mehdi Tabasi Hosein Shaddel -
Open Access Article
27 - Market-based Method for Reconfiguration of Distribution Networks Using Mine Blast Algorithm (MBA)
Sajjad Niroomand Alireza Bakhshinejad Mehdi Tabasi -
Open Access Article
28 - A New Method to Improve Energy Consumption in Wireless Camera Sensor Networks
javad bayat Shiva KarimiIntroduction: In the development of wireless camera sensor networks, there are unique challenges such as the need for high bandwidth, low latency for processing, high energy consumption, and real-time control. Each wireless camera sensor node is able to process image da MoreIntroduction: In the development of wireless camera sensor networks, there are unique challenges such as the need for high bandwidth, low latency for processing, high energy consumption, and real-time control. Each wireless camera sensor node is able to process image data locally and extract suitable data and cooperate with other cameras based on the desired application. In these networks, high bandwidth is demanded to transmit visual data, and high volume calculations in these networks must be possible with low power. In this article, a new model based on Harris‘s Hawk optimization algorithm is proposed to improve energy consumption in wireless camera sensor networks. The optimization algorithm of Harris‘s Hawk is one of the meta-heuristic algorithms that was invented in 2019. Method: Harris‘s Hawk optimization algorithm was used to form optimal clustering. Each vector generated in Harris‘s Hawk optimization algorithm is calculated based on the fitness function and the most optimal vectors are selected for clustering. In the proposed model, factors such as intra-cluster distance and extra-cluster distance, and energy consumption have been considered. Results: Evaluations in the environment of 150×150 m2 and 300×300 m2 with a different number of nodes show that the proposed model has better efficiency compared to PADT and genetic algorithm (GA). Discussion: In wireless camera sensor networks, the imbalance of energy consumption among nodes is an effective factor in the network lifetime. In order to balance the energy consumption among nodes, clustering algorithms have been proposed for uniform energy distribution. In this paper, we proposed a new model for clustering camera sensor nodes based on Harris‘s Hawk optimization algorithm. In the proposed model, we paid attention to parameters such as intra-cluster distance, extra-cluster distance, and residual energy of sensor nodes. The cluster quality criterion is based on the intra-cluster distance, which depends on the position of the cluster head in the clusters. In the proposed model, because the distance criterion is taken into account and the distance of non-cluster nodes with the cluster head node is evaluated and the closest nodes to the cluster head are selected. Manuscript profile -
Open Access Article
29 - Improving the Stability of a Power System Including SVC Based on Energy Function Minimization in a Multi-Model Optimal Coordinated Control Structure
Elaheh Pagard Shahrokh Shojaeian Mohammad Mahdi RezaeiIn this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controlle MoreIn this paper, the improvement of low frequency oscillation (LFO) damping in a power system including SVC is investigated. To achieve this goal, a new control strategy has been presented in which the multi-model controller is optimized using the linear optimal controller (LOC) and the particle swarm algorithm (PSO). The control bank in the multi-model controller includes three LOC controllers that generate optimal signals through the linearization of the nonlinear equations of the system and the minimization of an energy function to be combined by the Bayes recursive algorithm simultaneously to the generator excitation system and SVC. In order to generate an optimal linear signal, Riccati's equation must be solved; Riccati's equation includes two weight matrices Rric and Qric. These matrices elements are optimized by PSO algorithm. The PSO algorithm has calculated the optimal Rric and Qric with two different objective functions of maximizing the eigenvalues and minimizing the area under the speed curve. To evaluate the MMC-LOC-PSO control strategy, the symmetrical three-phase error is applied to the worst bus and the results of these two objective functions are compared. The simulation of the single machine power system has been done by MATLAB. The proposed control strategy, while maintaining stability, also effectively damps the LFOs, in addition, the permanent rotor speed and rotor angle error have also been favorably pushed to zero. Manuscript profile -
Open Access Article
30 - Presenting an economic objective function to improve the voltage profile in distributed generation systems based on arithmetic optimization algorithm
Seyyed Vahid Ziaratnia Seyyed Abed HosseiniThe use of distributed generation (DG) systems has significant effects, such as increasing the stability of the voltage profile, reducing power losses, and solving problems related to voltage stability. This study proposes an objective function to locate and determine t MoreThe use of distributed generation (DG) systems has significant effects, such as increasing the stability of the voltage profile, reducing power losses, and solving problems related to voltage stability. This study proposes an objective function to locate and determine the optimal size in DG systems. The objective function is based on increasing the voltage profile, reducing power loss, and improving economic efficiency using an arithmetic optimization algorithm (AOA). The proposed approach uses the lowest losses and improvement of the voltage profile level after injecting power into the system in distribution networks and sub-transmission networks to determine and locate the optimal size in DG systems. This research has been implemented on an IEEE 33-bus network using AOA, and the results are compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the proposed approach is superior to other optimization methods in locating and determining the optimal size, power loss, and cost function in DG systems. For example, AOA has obtained more profit than PSO and GA, 33.4% and 32.8%, respectively. In particular, the results show that AOA has a higher convergence speed in finding the optimal location in DG systems. Manuscript profile -
Open Access Article
31 - Increasing the efficiency of solar trackers by honey bee optimization algorithm
Hadieh Sadat Hosseini Amangaldi Koochaki Masood RadmehrMost of Control systems that often used in solar trackers, use signals from the solar radiation sensorwere placed on photovoltaic panels and control mutation of panel’s motors. Since, Sun information islimited and real time measurement is difficult, the solar trac MoreMost of Control systems that often used in solar trackers, use signals from the solar radiation sensorwere placed on photovoltaic panels and control mutation of panel’s motors. Since, Sun information islimited and real time measurement is difficult, the solar tracking algorithms developed withoutadditional hardware and sensors. This paper presents a method for determining the tilt and azimuthangle trajectories based on Bee Optimization Algorithm for PVs in order to achieve maximum outputenergy. Open-loop two axis sun tracking system is considered. The results show an increasing inenergy obtained by BA compared with Differential Evolution algorithm. Manuscript profile -
Open Access Article
32 - Applying Optimized Mathematical Algorithms to Forecast Stock Price Average Accredited Banks in Tehran Stock Exchange and Iran Fara Bourse
Negar Aghaeefar Mohammad Ebrahim Mohammad Pourzarandi Mohammad Ali Afshar Kazemi Mehrzad Minoie -
Open Access Article
33 - Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso)
Aliasghar Davoodi Kasbi Iman Dadashi -
Open Access Article
34 - Support Vector Regression Parameters Optimization using Golden Sine Algorithm and Its Application in Stock Market
Mohammadreza Ghanbari Mahdi Goldani -
Open Access Article
35 - Integration of order preparation process in warehouse and distribution to production lines to minimize cost with adaptive whale algorithm approach
Amir Reza Ahmadi Keshavarz davood jaafari mehran khalaj Parshang DokouhakiOne of the most costly logistics activities is the picking process in the warehouse. Considering the internal logistics aspects, due to the limitations and resources available in order to reduce costs by increasing the level of capability, the supply systems of material MoreOne of the most costly logistics activities is the picking process in the warehouse. Considering the internal logistics aspects, due to the limitations and resources available in order to reduce costs by increasing the level of capability, the supply systems of materials and components will be achieved along the line. Considering the effect of the completion time of pick operations on the start time of distribution operations and the cost of order preparation tardiness, the present study aimed to investigate a new issue related to the integrated process of order preparation in the warehouse and delivery on time to minimize cost according to the data of a car companys. In this regard, an integer nonlinear programming model is proposed to minimize the costs caused by tardiness. In order to validate the model, the small problem is solved in exact way. To solve the model, since the problem is NP-Hard, the method of whale optimization algorithm was used and to improve the optimal routing solutions, the problem was investigated by designing an adaptive whale algorithm considering the cost and time of visiting workstations as a fitting function. Also, to assess the proposed adaptive whale algorithm, the results were compared with two meta-heuristic algorithms of particle swarm optimization and gray wolf. The results show that the proposed adaptive wall algorithm performs better than other methods, which improves and reduces costs. Manuscript profile -
Open Access Article
36 - Solving Resource-Constrained Project Scheduling Problem with Particle Swarm Optimization (Case Study: Bandar Abbas Gas Condensate Refinery)
Mohammadhusein Nabizadeh Huseinali Hasanpoor Roozbeh Azizmohammadi Navid HashtroodiOne of the issues considered by the projects responsible especially project managers is the execution of project activities according to time schedule. The very difficult nature of that issue is also another reason for the researchers to take much note of it. Therefore, MoreOne of the issues considered by the projects responsible especially project managers is the execution of project activities according to time schedule. The very difficult nature of that issue is also another reason for the researchers to take much note of it. Therefore, there are especial techniques and methods to solve those issues. Also, project managers pay much attention to the stability of the time schedule as it is important for them. This paper is provided with a real project time schedule for a refinery by using stable time schedule. Particle swarm optimization algorithm is suggested to resolve the problem since the project time schedule has resources limitation including NP- Hard. In order to accesses the validation of the model, 4 issues with small scales has been selected and the results from the suggested algorithm was compared with the accurate result obtained from lingo software. These results indicate that the suggested algorithm is effective and convergent with the optimized result. Manuscript profile -
Open Access Article
37 - Multi-objective Portfolio Optimization Model by Fruit Fly Optimization Algorithm
Amir Amini alireza alinezhadOne of the most famous optimization problems in the field of financial engineering is portfolio selection problem. In its simplest form, while trying to minimize risk in the portfolio selection according to defined constraints such as budget and integer constraints it d MoreOne of the most famous optimization problems in the field of financial engineering is portfolio selection problem. In its simplest form, while trying to minimize risk in the portfolio selection according to defined constraints such as budget and integer constraints it deals with selecting a basket of various assets. Generally, investors prefer to invest in some assets rather than investing in only one asset to reduce unsystematic risk by diversifying their investment. Complex computational models have been developed to solve this problem and there is not an optimal solution for many of them. In this paper, a new and innovative approach known as fruit fly optimization algorithm (FOA) is used for multi-objective problem solving based on mean-variance Markowitz problem with class and cardinality constraints. Fruit fly optimization algorithm is a new way to find the overall optimal solution based on the behavior of the fruit fly in finding food. So far, few studies have been done on this algorithm and almost none of them used this algorithm for portfolio optimization problem. The results indicated the better comparative performance of the algorithm compared to the genetic algorithm for data set of Tehran stock exchange.JEL classification: G1, P5, O3 Manuscript profile -
Open Access Article
38 - Predicting distribution pattern of Bemisia tabaci G. ( (Hem.: Aleyrodidae) by Hybrid neural network With Particle Swarm Optimization Algorithm
Alireza Shabaninejad Bahram TafaghodiniyaToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm MoreToday, with the Advance statistical techniques and neural networks, predictive models of distribution was rapidly developed in Ecology. Purpose of this study was predict and Mapping distribution of Bemisia tabaci G. using MLP neural networks combined with Particle Swarm Optimization in surface of cucumber field. Population data of pest was obtained in 2017 by sampling in 100 fixed points in a fallow field in Ramhormoz, to evaluate the ability of neural networks combined with Particle Swarm Optimization to predict the distribution used statistical comparison parameters such as mean, variance, statistical distribution and coefficient determination of linear regression among predicted values and actual values. Results showed that in training and test phases of neural network combined Particle Swarm Optimization algorithm, was no significant effect between variance, mean and statistical distribution of actual values and predicted values. Our map showed that patchy pest distribution offers large potential for using site-specific pest control on this field. Manuscript profile -
Open Access Article
39 - Application of ant colony optimization method in GIS
Mohsen Ghods Hossein Aghamohammadi Alireza Vafaei Nejad Alireza Gharagozlu Saeed BehzadiSwarm intelligence is one of the new growing methods that is considered in artificial intelligence as a function of the social interaction of components. The Basics of swarm intelligence are based on the study of the behavior of social organisms such as some insects (be MoreSwarm intelligence is one of the new growing methods that is considered in artificial intelligence as a function of the social interaction of components. The Basics of swarm intelligence are based on the study of the behavior of social organisms such as some insects (bees, ants, termites) or even humans. The issue of using meta-heuristic methods for application in hybrid optimization problems is a rapidly growing field of research. This is due to the importance of hybrid optimization issues in the world of industry and science. In recent years, one of the most important and promising researches has been "supra-innovative methods derived from nature", which has had very good results in solving problems of combined problems. Meta-heuristic algorithms are used to solve a problem when, as the size of the problem increases dramatically, so-called NP-hard problems. One of the most widely used meta-innovative methods in this field is the ant colony optimization algorithm, which is used today in solving the problems of spatial resource allocation, routing, and location in GIS environments. In this research, while examining the ant colony algorithm, its expression and parameters required for use in the GIS environment are discussed. The ability of algorithms based on food search in the ant colony algorithm is significantly dependent on the optimal determination of the parameters in these algorithms. Manuscript profile -
Open Access Article
40 - A Combinatory Feature Selection Method using Gray Wolf Optimization and Crow Search Algorithms for Intrusion Detection Systems
Kayvan Asghari -
Open Access Article
41 - Optimization of a thermoelectric refrigeration system to enhance cooling capacity
Amin Hadidi -
Open Access Article
42 - Electrical Energy Storage on the Hybrid Grid of Renewable Energy System Using Fuzzy Controller Optimization Algorithm
Parviz Ghoflghari Hossein Nasiraghdam -
Open Access Article
43 - Load Frequency Control in Power Systems Using Improved Particle Swarm Optimization Algorithm
Milad Babakhani Qazijahan -
Open Access Article
44 - Black Widow Optimization (BWO) Algorithm in Cloud Brokering Systems for Connected Internet of Things
Nasim Jelodari Ali AsgharPourhaji Kazem -
Open Access Article
45 - Increasing Lifetime Using Whale Optimization Routing Algorithm in Wireless Sensor Networks
Hassan Nouri Esmaeil Zeinali -
Open Access Article
46 - A Review of Feature Selection Method Based on Optimization Algorithms
Zohre Sadeghian Ebrahim Akbari Hossein Nematzadeh Homayun Motameni -
Open Access Article
47 - Text Summarization Using Cuckoo Search Optimization Algorithm
Seyed Hossein Mirshojaei Behrooz Masoomi -
Open Access Article
48 - Developing and Solving two Level Lot Sizing Problem with Multi Production Methods using Vibration Damping Optimization Algorithm
Mohammad Ebrahimi Maghsod AmiriThe Capacitated Lot Sizing Problem (CLSP) consists of determining the production quantity and timing for several items on a single facility over a finite number of periods so that the demand and capacity constraints can be satisfied at a minimum cost. In this Article, d MoreThe Capacitated Lot Sizing Problem (CLSP) consists of determining the production quantity and timing for several items on a single facility over a finite number of periods so that the demand and capacity constraints can be satisfied at a minimum cost. In this Article, developing two level lot sizing problem with multi production methods is provided. The objective of the proposed model is to minimize costs. Vibration Damping Optimization (VDO) is used to solve a model. Taguchi method has been utilized to calibrate the parameters of algorithms Since the quality of solving all of the meta-innovative algorithms depends on their parameters.Then, to demonstrate the proper function of the solution method is provided, at first, experimental issues with different dimensions were generated, then it was solved by Lingo software and Vibration Damping Optimization. Finally, we compare the Lingo response and the optimization algorithm to reduce the vibration damping Optimization algorithm together in terms of the solution time. The results show that the answer to the vibration damping Optimization algorithm has a better quality than Lingo in issues of large size. Manuscript profile -
Open Access Article
49 - Hub Covering Location Problem Considering Queuing and Capacity Constraints
Mehdi Seifbarghy Mojtaba Hemmati Sepideh Soltan Karimi -
Open Access Article
50 - A Hybrid Method for Industrial Robot Navigation
Somayeh Raiesdana -
Open Access Article
51 - A new multi-objective mathematical model for a Citrus supply chain network design: Metaheuristic algorithms
M.B. Fakhrzad F. Goodarzian -
Open Access Article
52 - Optimizing Inventory Management Costs in Supply Chains by Determining Safety Stock Placement
Abdollah Arasteh -
Open Access Article
53 - Developing and solving the multi-objective flexible and sustainable job shop scheduling problem with reverse flow and job rotation considerations in uncertain situations
Arsalan Shojaei Davood Jafari Mehran Khalag Parshang Dokohaki -
Open Access Article
54 - Optimization of Stand-alone Hybrid PV/Wind/Fuel-Cell System Considering Reliability Indices Using Cuckoo Optimization and Firefly Algorithm
Mehdi Rezaei محمود قنبری -
Open Access Article
55 - Forward and Inverse Kinematics of 4-DoF SCARA: Using Optimization Algorithms
Mahdi Zavar Niki Manouchehri Alireza Safa -
Open Access Article
56 - Joint Pricing and Inventory Routing Modeling in a Two Echelon Closed Supply Chain
mohamad mohamadnejad Isa nakhaei kama abadi Ramin Sadeghian Fardin Ahmadi zarAbstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximi MoreAbstract This paper studies the pricing issue in a multi-period and multi-product closed-loop supply chain with price-dependent demands. The aim is to assign a location for the collection and disassemble center, vehicle routing, and material ordering in order to maximize the profit. In the study, a non-linear mathematical model is presented for small scale problems. Due to the NP-hardness of the problem, two met heuristics, genetic algorithm and particle swarm optimization algorithm, are applied to solve medium and large scale problems. The algorithms are validated by comparing their results with those of the mathematical model. Finally, the performance comparison of the two met heuristics through statistical analysis is demonstrated that the particle swarm optimization algorithm performance outperforms the genetic algorithm. Manuscript profile -
Open Access Article
57 - Solving the Problem of Scheduling Unrelated Parallel Machines with Limited Access to Jobs
Mohammadreza Naghibi Abolfazl Adressi -
Open Access Article
58 - Optimal Design of Residential Microgrids with Regard to Fault Occurrence and Possibility of Power Outage
Mehrdad Movahedpour Sirus Mohammadi Mohammadjavad Kiani Taher Niknam Mahmoud ZadehbagheriOne of the issues which has attracted a lot of attention in the power grid in recent years is the emergence of microgrids. An optimized microgrid design includes choosing the best combination of the available options (distributed generation units, energy storage systems MoreOne of the issues which has attracted a lot of attention in the power grid in recent years is the emergence of microgrids. An optimized microgrid design includes choosing the best combination of the available options (distributed generation units, energy storage systems, and load response programs) to supply the microgrid so that the total costs of the microgrid development plan is minimized. In this article, a comprehensive modeling has been conducted for the problem of optimal design of residential microgrids considering the renewable distributed generation units, energy storage systems and controllable loads. This model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models for them has been chosen. In order to find the optimal solution, the problem of microgrid design is modeled as an optimization problem with the goal of minimizing the total costs of the microgrid development plan and the optimal response is determined via ant colony optimization algorithm. Manuscript profile -
Open Access Article
59 - Apply a Mutation in Gray Wolf Optimization Algorithm to Solve the Economic-Environmental Dispatch Problem of Integrated Power Plants Including Thermal and Wind
Mahdi Afroozeh Hamidreza Abdalmohammadi Mohammad-Esmaeil NazariIn this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating c MoreIn this paper, a dynamic mutant version of the gray wolf optimization algorithm (MGWO) is proposed to solve the economic-environmental dispatch (E-ED) problem of a standard 40-unit power system with two wind farms. Thus, a comprehensive objective function of operating costs is presented, which is a combination of wind energy costs, over-estimated penalty costs, under-estimated penalty costs, thermal unit costs and emission costs. Due to the random nature of wind speed, the power generated by wind turbines is unpredictable. Therefore, the Weibull probability distribution function has been used to model the wind farm power in this paper. The cost of operating a wind farm is considered probabilistic so that low-probability wind scenarios have less effect on the total operation cost. The simulations are performed in the form of three section and the optimization results are compared with several meta-heuristic algorithm results for validation. The results of the optimizations in all three scenarios and its comparison with other algorithms confirm the better performance and higher accuracy of the proposed MGWO algorithm than the original version of the gray wolf algorithm (GWO) as well as other algorithms. Manuscript profile -
Open Access Article
60 - Optimal Design, Modeling, and Evaluation of Single-Phase Axial Flux Induction Motor with a Permanent Capacitor Using Improved Particle Swarm Optimization Algorithm (IPSO)
Amin Aboutalebi NajafabadiThe increasing application of single-phase axial flux induction motors with a permanent capacitor and their low efficiency has led to the importance of optimization of this type of motors. In this paper, by introducing the classical algorithms of design of this type of MoreThe increasing application of single-phase axial flux induction motors with a permanent capacitor and their low efficiency has led to the importance of optimization of this type of motors. In this paper, by introducing the classical algorithms of design of this type of motors, which consists of finding the dimensions of different parts of the motor and calculation of electrical parameters such as resistance and reactance, and capacitor, by introducing the proposed equivalent circuit in the permanent state to reduce the air gap of the motor, introduces the structure of optimization algorithms and then uses a genetic algorithm and improved particle swarm algorithm to optimize the design of the axial flux motor to increase efficiency, increase power factor and reduce core volume. For this purpose, a single-phase axial flux induction motor with a permanent capacitor that has considerable application in ventilation systems is investigated, and using design formulas and with the help of a circuit equivalent to the proposed permanent state, as well as using Intelligent methods such as genetic algorithm and improved particle swarm algorithm, engine optimization to increase maximum efficiency and the results are drawn in the form of torque-speed and efficiency-speed diagrams and compared with each other. Finally, the designed motor is simulated by the finite element method to verify the design algorithm, the steady-state model, the proposed optimization algorithm, and the test results. Manuscript profile -
Open Access Article
61 - Wireless Sensor Networks Routing Using Clustering Based on Multi-Objective Particle Swarm Optimization Algorithm
Seyed Reza Nabavi Nafiseh Osati Eraghi Javad Akbari TorkestaniWith the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks MoreWith the spread of applications of wireless sensor networks, in recent years, the use of this type of network in order to monitor the environment and analyze data collected from specific environments in a variety of ways has become very common. Wireless sensor networks are one of the best options for collecting data from the environment due to their easy configuration and no need for expensive equipment. The energy of sensors in wireless sensor networks is limited, which is a major challenge due to the lack of a fixed charge source. Because most of the sensors' energy is wasted during data transmission, a sensor that transmits more data than others and transmits data over long distances with packets will run out of energy sooner than others. When a sensor in the network runs out of energy, the network process may be disrupted. Therefore, due to the dynamic topology and distributed nature of wireless sensor networks, designing energy efficient routing protocols is one of the main challenges. Therefore, in this article, energy-aware routing protocol based on multi-objective particle swarm optimization algorithm is presented. In the proposed approach, the fitness function of the particle swarm optimization algorithm for selecting the optimal cluster head based on quality-of-service goals including residual energy, link quality, end-to-end delay and delivery rate. The simulation results show that the proposed approach has less energy consuming and extend network lifetime due to balancing the goals of quality-of-service criteria than other approaches. Manuscript profile -
Open Access Article
62 - Optimal Design of a Hybrid Solar–Wind–Battery System using the Grasshopper Optimization Algorithm for Minimization of the Loss of Power Supply Probability
Ronak Jahanshahi Bavandpour Hamid Ghadiri Hamed KhodadadiRenewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an afforda MoreRenewable energy has been developed in recent years due to the limited sources of fossil fuels, their possibility of depletion, and the related environmental issues. The main challenges of these type of systems is reaching to the optimum size in order to have an affordable system based on storing the solar and wind energy. In this paper, optimization of a solar-wind hybrid system is presented with a saving battery system for supplying a specific hourly load annually to minimize annual system expenses and the probability of Loss of Power Supply Probability (LPSP). Annual expenses of the system include initial investment, maintenance, and replacement costs. The purpose of optimization is to determine the numbers of solar panels, wind turbines, batteries, the height of the wind tower, and the angle of the solar panel toward solar radiation. For this issue, a new method named Grasshopper Optimization Algorithm (GOA) is employed. Also, the effects of changes in inverter efficiency, load demand, and of maximum probability of LPSP on system designing are evaluated. Simulation results show that the efficiency reduction, load increase, and increasing the load and maximum reliability in the system in the form of reducing of LPSP lead to an increase in annual energy costs of systems. Furthermore, the results indicate the superiority of the GOA method toward particle swarm optimization (PSO) in reaching better target function and less cost. Manuscript profile -
Open Access Article
63 - The Electricity Consumption Prediction using Hybrid Red Kite Optimization Algorithm with Multi-Layer Perceptron Neural Network
Jalal Raeisi-Gahruei Zahra BeheshtiSince the electricity consumption’s prediction is one of the most important aspects of energy manage­ment in each country, various methods based on artificial intelligence have been proposed to manage it. One of these methods is Artificial Neural Networks (ANN MoreSince the electricity consumption’s prediction is one of the most important aspects of energy manage­ment in each country, various methods based on artificial intelligence have been proposed to manage it. One of these methods is Artificial Neural Networks (ANN). To improve the performance of ANNs, an efficient algorithm is necessary to train it. Back Propagation (BP) algorithm is the most common algorithm employed in training ANNs, which is based on gradient descent. Since BP may fall in local optima, it cannot provide a good solution in some problems. To overcome this shortcoming, optimiz­ation algorithms like meta-heuristic algorithms can be applied to train ANNs. In this study, a new meta-heuristic algorithm called Red Kite Optimization Algorithm (ROA) is introduced, which is inspired by the social life of red kites in nature. The ROA has several advantages such as simplicity in structure and implementation, having few parameters and good convergence rate. The perfprmance of ROA is compared with some recent metaheuristic algorithms on benchmark functions of CEC2018. Also, it is employed to train Multi-Layer Perceptron (MLP) for the electricity consumption prediction at peak load times in Iran. The results show the good performance of proposed algorithm compared with competitor algorithms in terms of solution accuracy and convergence speed. Manuscript profile -
Open Access Article
64 - Increase the Efficiency of the Offloading Algorithm in Fog Computing by Particle Swarm Optimization Algorithm
Seyed Ebrahim Dashti Hoasain ZareEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage op MoreEdge computing is a computing paradigm that extends cloud services to devices at the edge. This processing model refers to technologies that allow computing and storage to be performed on devices at the edge of the network. In this architecture, computing and storage operations take place close to objects and data sources. In order to reduce latency and network traffic between end devices and cloud centers, groups at the edge have processing capabilities, perform a large number of processing and computing tasks, including data processing, temporary storage, device management, decision making, and privacy protection. Since the number of edge devices is large, there must be a mechanism to select these tasks and offload them to the cloud. The problem to be decided is that which one of the available edge devices should be selected for unloading and then unloaded. This problem is classified as one of the hard non-polynomial problems and by using deterministic algorithms simply and in polynomial time, it is not possible to find a suitable and efficient solution for it found. Manuscript profile -
Open Access Article
65 - Intelligent Control of UPFC for Enhancing Transient Stability on Multi-Machine Power Systems
Hassan Barati Reza Saki Seyed Saeeidolah MortazaviOne of the benefit of FACTS devices is increase of stability in power systems with control active and reactive power at during the fault in power system. Although, the power system stabilizers (PSSs) have been one of the most common controls used to damp out oscillation MoreOne of the benefit of FACTS devices is increase of stability in power systems with control active and reactive power at during the fault in power system. Although, the power system stabilizers (PSSs) have been one of the most common controls used to damp out oscillations, this device may not produce enough damping especially to inter-area mode and therefore, there is an increasing interest in using FACTS devices to aid in damping of these oscillations. In This paper, UPFC is used for damping oscillations and to enhance the transient stability performance of power systems. The controller parameters are designed using an efficient version of the Takagi-Sugeno fuzzy control scheme. The function based Takagi-Sugeno-Kang (TSK) fuzzy controller uses. For optimization parameters of fuzzy PI controller, the GA, PSO and HGAPSO algorithms are used. The computer simulation results, the effect of UPFC with conventional PI controller, fuzzy PI controller and intelligent controllers (GA, PSO and HGAPSO) for damping the local-mode and inter-area mode of under large and small disturbances in the four-machine two-area power system evaluated and compared. Manuscript profile -
Open Access Article
66 - Optimal PID Controller Tuning for Multivariable Aircraft Longitudinal Autopilot Based on Particle Swarm Optimization Algorithm
Mostafa Lotfi Forushani Bahram Karimi Ghazanfar ShahgholianThis paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required MoreThis paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis) based on particle swarm optimization (PSO) algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller. Manuscript profile -
Open Access Article
67 - Fractional Order PID Controller Design for Level Control of Three Tank System Based on Improved Cuckoo Optimization Algorithm
Meysam Gheisarnezhad Hamed MojallaliFractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared with the Standard PID controller, two adjustable variables “differential order” and “integral order” are added to the MoreFractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared with the Standard PID controller, two adjustable variables “differential order” and “integral order” are added to the PID controller.Three tank system is a nonlinear multivariable process that is a good prototype of chemical industrial processes. Cuckoo Optimization Algorithm (COA), that was recently introduced has shown its good performance in optimization problems. In this study, Improved Cuckoo Optimization Algorithm (ICOA) has been presented. The aim of the paper is to compare different controllers tuned with a Improved Cuckoo Optimization Algorithm (ICOA) for Three Tank System. In order to compare the performance of the optimized FOPID controller with other controllers, Genetic Algorithm(GA), Particle swarm optimization (PSO), Cuckoo Optimization Algorithm (COA) and Imperialist Competitive Algorithm (ICA). Manuscript profile -
Open Access Article
68 - Multilayer Paraboloid Structures Optimization of Using a Hybrid Charged System Search
Amir abbaspour siamak Talaat ahariSpace structure is a rigid, lightweight, truss-like structure constructed from interlocking struts in a geometric pattern. Space structure can be covered large areas without intermediate supports.In this paper, the problem of simultaneous shape and size optimization of MoreSpace structure is a rigid, lightweight, truss-like structure constructed from interlocking struts in a geometric pattern. Space structure can be covered large areas without intermediate supports.In this paper, the problem of simultaneous shape and size optimization of a three-layer paraboloid space structure is addressed. In this method, the hybrid charged system search-particle swarm is utilized as the optimization algorithm and the result is compared with the particle swarm optimization algorithm. The objective of this paper is to find optimal weight, that design variables are considered as height and cross-sectional area. For conducting this, a three-layer paraboloid space structure is designed by SAP and then optimized by using hybrid charged system search-particle swarm and particle swarm optimization algorithms. The result demonstrate the efficiency of the hybrid charged system search-particle swarm algorithm. Manuscript profile -
Open Access Article
69 - Development of a two-stage method based on optimization algorithms and smart calculation methods in structural damage detection
Behrouz Safa Asghar Rasouli Yahya NasiraAmong the countless methods that have been proposed in the field of structural damage detection, the finite element model updating method has been very popular. However, the accuracy and efficiency of this method decrease drastically when the number of variables in the MoreAmong the countless methods that have been proposed in the field of structural damage detection, the finite element model updating method has been very popular. However, the accuracy and efficiency of this method decrease drastically when the number of variables in the problem increases, and this is a problem when dealing with large structures with a large number of elements. In this research, a two-step method is proposed, which is capable of reducing the size of the damage detection problem introduced to the updated model by identifying damaged structural members through a damage index based on static strain energy in the first step. Therefore, only a few variables are introduced to the second step, which include a process of updating the finite element model. This second step actually consists of an iterative process of updating the model, which uses a new and damage-sensitive objective function to detect the severity of damage in the elements identified in the previous step. Also, a meta-exploratory optimizer named equilibrium optimizer is utilized to determine the value of the unknown variables of the problem, which are the damage values of the elements introduced by the first step. The proposed method has also been tested on a number of numerical samples to check the effectiveness of the method in the presence of external disturbing factors such as measurement noise. A comparative study has been done to compare the results. According to the results, the proposed method is able to detect the location and severity of damage in different structures, and measurement noises and modal information only from the first few vibration modes do not have much impact on the accuracy of the results. A laboratory study has also been conducted to find out the efficiency and accuracy of the proposed method in real structures, and according to the results, the proposed method is well able to detect damage. Manuscript profile -
Open Access Article
70 - Development of a two-stage method based on optimization algorithms and smart calculation methods in structural damage detection
Behrouz Safa Asghar Rasouli Yahya NasiraAmong the countless methods that have been proposed in the field of structural damage detection, the finite element model updating method has been very popular. However, the accuracy and efficiency of this method decrease drastically when the number of variables in the MoreAmong the countless methods that have been proposed in the field of structural damage detection, the finite element model updating method has been very popular. However, the accuracy and efficiency of this method decrease drastically when the number of variables in the problem increases, and this is a problem when dealing with large structures with a large number of elements. In this research, a two-step method is proposed, which is capable of reducing the size of the damage detection problem introduced to the updated model by identifying damaged structural members through a damage index based on static strain energy in the first step. Therefore, only a few variables are introduced to the second step, which include a process of updating the finite element model. This second step actually consists of an iterative process of updating the model, which uses a new and damage-sensitive objective function to detect the severity of damage in the elements identified in the previous step. Also, a meta-exploratory optimizer named equilibrium optimizer is utilized to determine the value of the unknown variables of the problem, which are the damage values of the elements introduced by the first step. The proposed method has also been tested on a number of numerical samples to check the effectiveness of the method in the presence of external disturbing factors such as measurement noise. A comparative study has been done to compare the results. According to the results, the proposed method is able to detect the location and severity of damage in different structures, and measurement noises and modal information only from the first few vibration modes do not have much impact on the accuracy of the results. A laboratory study has also been conducted to find out the efficiency and accuracy of the proposed method in real structures, and according to the results, the proposed method is well able to detect damage. Manuscript profile -
Open Access Article
71 - مدلسازی و بهینهسازی زنجیره تامین کودهای شیمیایی با استفاده از ترکیب بهینهسازی نهنگ و شبیهسازی تبرید
Motahareh Rabbani سید محمد حاجی مولانا Seyed Mojtaba Sajadi Mohammad Hossein Davoodiفسفر مهمترین ماده مورد استفاده در کودهای شیمیایی است و نقش اساسی در افزایش عملکرد محصول در سیستمهای کشاورزی ایفا می کند. با توجه به افزایش تقاضا برای فسفر و منابع محدود این ماده حیاتی، مدیریت زنجیره تامین پایدار کودهای شیمیایی از اهمیت بالایی برخوردار است. در مطالعه حا Moreفسفر مهمترین ماده مورد استفاده در کودهای شیمیایی است و نقش اساسی در افزایش عملکرد محصول در سیستمهای کشاورزی ایفا می کند. با توجه به افزایش تقاضا برای فسفر و منابع محدود این ماده حیاتی، مدیریت زنجیره تامین پایدار کودهای شیمیایی از اهمیت بالایی برخوردار است. در مطالعه حاضر، یک مدل ریاضی برای زنجیره تامین کودهای شیمیایی ارائه شده است. با در نظر گرفتن اثرات نامطلوب زیست محیطی تولید و مصرف کودهای شیمیایی، پژوهش حاضر سعی در طراحی یک زنجیره تامین پایدار با توجه به عوامل اقتصادی، زیست محیطی و اجتماعی دارد. برای حل این مسئله، یک الگوریتم فراابتکاری ترکیبی شامل بهینهسازی نهنگ و شبیهسازی تبرید با در نظر گرفتن یک تابع چندهدفه استفاده میشود. نتایج شبیهسازی بهدستآمده از یک مطالعه موردی واقعی شبکه زنجیره تامین کودهای شیمیایی در ایران، اثربخشی و کاربرد مدل و راهحل پیشنهادی را اثبات میکند. نتایج بهدستآمده نشاندهنده اثربخشی روش پیشنهادی در مقایسه با سایر الگوریتمها با توجه به عوامل اقتصادی، اجتماعی و محیطی است. Manuscript profile -
Open Access Article
72 - Optimization of Taleghan Dam Reservoir Operation Using Grey Wolf Algorithm and Its Hybrid with Genetic Algorithm
ardavan davani motlagh Mohammad Sadegh Sadeghian Amir Hossein Javid Mohammad Sadegh AsgariDue to population growth, shortage and severe limitation of water resources, one of the basic steps in water management and planning is reservoir optimization. In the present study, after the introduction of the Gray Wolf optimization algorithm, the performance of this MoreDue to population growth, shortage and severe limitation of water resources, one of the basic steps in water management and planning is reservoir optimization. In the present study, after the introduction of the Gray Wolf optimization algorithm, the performance of this algorithm alone and in combination with the genetic algorithm in optimizing the operation of the Taleghan Dam reservoir has been evaluated. The objective function is to minimize the total squares of relative deficiencies in allocating to it each month and maximize reliability throughout the 11-year transition period from 2009 to 2017. Also, the constraints of reservoir continuity equation, reservoir storage volume and reservoir release volume were applied to the objective function of the problem. The results obtained from the performance evaluation indices of the models showed that in terms of time reliability, vulnerability and sustainability indices, the gray wolf-genetic hybrid algorithm with 72.73, 0.28, 24.66 is better than the gray wolf algorithm with 68.93, 0.29, 21.48 and the algorithm. Genetics with 66.66, 0.41, 21.34. Manuscript profile -
Open Access Article
73 - An Improved Algorithmic Method for Software Development Effort Estimation
Elham Khatibi Vahid Khatibi Bardsiri -
Open Access Article
74 - A Hybrid Optimization Algorithm for Learning Deep Models
Farnaz Hoseini Asadollah Shahbahrami Peyman Bayat -
Open Access Article
75 - Localization of Underwater Wireless Sensor Network Nodes Using Cuckoo Optimization Algorithm
Leila Falahatpisheh -
Open Access Article
76 - IKM-SARAVOA: A New Hybrid-based Search and Rescue Algorithm with African Vulture Optimization Algorithm for Data Clustering
Ehsan Soleimani Dehkordi Mohammadreza Mollahoseini Ardakani -
Open Access Article
77 - Cuckoo Optimization Algorithm in Cutting Conditions During Machining
Ahmad Esfandiari -
Open Access Article
78 - Intelligent Determining Amount of Inter-Turn Stator Winding Fault in Permanent Magnet Synchronous Motor Using an Artificial Neural Network Trained by Improved Gravitational Search Algorithm
Mehran Taghipour-gorjikolaie Seyyed Mohammad Razavi Mohammad Ali ShamsiNejad -
Open Access Article
79 - Solving Flexible Job-Shop Scheduling Problem using Hybrid Algorithm Based on Gravitational Search Algorithm and Particle Swarm Optimization
Behnam Barzegar Homayun Motameni -
Open Access Article
80 - Optimal Design of Three Phase Surface Mounted Permanent Magnet Synchronous Motor by Particle Swarm optimization and Bees Algorithm for Minimum Volume and Maximum Torque
Sahra Khazaei Abdolhossein Tahani Mohammad Yazdani-Asrami S. Asghar Gholamian -
Open Access Article
81 - Multi-objective Placement of Capacitor Banks in Distribution System using Bee Colony Optimization Algorithm
Abbas Baghipour Saeed Fallahian -
Open Access Article
82 - بهینه سازی برنامه ریزی تولید با استفاده از الگوریتم ژنتیک و بهینه سازی ذرات (مطالعه موردی: کارخانه چای صوفی)
منصور صوفی مریم محسنی -
Open Access Article
83 - Effects of Atmospheric Changes on Reducing the Performance of Solar Panels by Particle Swarm Optimization Algorithm
Shahrokh Jalili Elay Mehrpourazari -
Open Access Article
84 - A Facility Location Problem in a Green Closed-Loop Supply Chain Network Design by Considering Defective Products
Zahra Zanjani Foumani Ensieh Ghaedy heidary Amir Aghsami Masoud Rabbani -
Open Access Article
85 - A Novel Hybrid Approach to Enhance Intelligence Integration in Small-Medium Enterprises
Hamidreza Seifi Kaveh Mohammad Cyrus Naser Shams Gharneh -
Open Access Article
86 - Optimum Design of Reinforced Concrete Cantilever Retaining Walls by Cuckoo Optimization Algorithm (COA)
Mehdi Shalchi Tousi Samane Laali -
Open Access Article
87 - Improving Reliability by Optimal Allocation of Protection Devices and Distributed Generation Units
حمیدرضا اکبری Amirhosein Bolurian Mahmoud Modaresi -
Open Access Article
88 - Optimal and Intelligent Designing of Stand-alone Hybrid Photovoltaic/Wind/Fuel Cell System Considering Cost and Deficit Load Demand Probability, Case Study for Iran (Bushehr City)
Eisa Ansari Nezhad Mojtaba Najafi -
Open Access Article
89 - Energy Optimization in Smart Grids Using Whale Optimization Algorithm and Fuzzification
Marzieh Poshtyafteh Afshin Lashkarara Hasan Barati -
Open Access Article
90 - .Application of Meta-Heuristic Algorithms in Predicting Financial Distress using intra-corporate (Financial and non-financial) and Economic Variables (Grasshopper Optimization and Ant Colony Algorithms)
فریدون مرادی احمد یعقوب نژاد آزیتا جهانشاد. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this rese More. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this research is improving the performance of the basic model of Multilayer Perceptron Artificial Neural Network (ANN-MLP) by-using a hybrid model with GOA (MLP-GOA) and Ant Colony Optimization Algorithm (MLP-ACO). The statistical research population of companies active in Tehran Stock Exchange during a 7-year period (from 1391 to 1397) included 476 companies, and finally, after systematic elimination, there were 289 qualified companies (including 2023 observation year-company). Checked and screened. The results showed the ability of ANN-MLP model to predict financial distress by-using financial and non-financial variables, and in addition the hybrid models (MLP-GOA and MLP-ACO) had been improved this ability. The accuracy of the MLP-GOA model for the year t, year t-1and year t-2 (before financial distress occurs), respectively are 97.30%, 94.53% and 91.30% that higher than the accuracy of the basic model and the hybrid MLP-ACO model. Although, entering the economic variables has increased the capability of all models significantly but the results showed that the financial distress is more affected by intra-corporate variables and the effect of economic variables has already been considered through the effect on financial events recorded in the accounting system. The results of this study can be used by company managers, banks and rating and credit institutions, insurance companies, financial analysts, investors and investment companies in assessing the risk of financial distress to make appropriate decisions and actions. Manuscript profile -
Open Access Article
91 - Predicting Negative Price Shock with Emphasis on Financial Ratios
Ebrahim Fadaii Mohammad Javad ZareBahnamiriAbstractAccording to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors MoreAbstractAccording to capital market research, the negative stock price shock in any market is a function of environmental factors and specific characteristics of the company, and any insight on how to describe and predict the shock can affect the decisions of investors and activists in the stock market. In this study, based on data related to 140 companies listed on the Tehran Stock Exchange.we have attempted to predict stock price shocks with emphasis on financial ratios. In order to select the optimal variables from the set of 96 variables, two evolutionary algorithms of particle swarm optimization and genetic algorithm have been used. After applying the mentioned algorithms, finally, 8 variables affecting permanent and temporary shocks were extracted, which in the regression model mentioned in the research, their effect on the predictor of shock was investigated. the results of RSME model are the permanent shock (genetic algorithm), permanent shock (particle swarm optimization), temporary shock (genetic algorithm) and temporary shock (particle swarm optimization (particle swarm optimization), 5.8433 , 5.6284 , 7.537 and 7.295 . as we observe , RSME in permanent shock based on genetic algorithm is more than RSME permanent shock model based on the evolutionary algorithm of particle swarm optimization. also in the transient shock model based on the genetic algorithm , the model is more than RSME of the temporary shock model based on the evolutionary algorithm of particle swarm optimization . It can therefore be stated that the estimated regression is based on the selected variables from the evolutionary algorithm of the particle swarm optimization, and has better predictive power than the selected variables of the genetic algorithm. Manuscript profile -
Open Access Article
92 - Fuzzy PID Tuned by a Multi-Objective Algorithm to Solve Load Frequency Control Problem
Ehsan Tehrani Amir Reza Zare Bidaki Mohsen Farahani -
Open Access Article
93 - Coordination Design of Power System Stabilizer and FACTS Controllers Using Nature-Inspired Metaheuristics Optimization Algorithms- A Brief Review
Sayed Mohammadali Zanjani Ghazanfar Shahgholian Arman Fathollahi Sayed Mohammad Hosain ZanjaniElectromechanical oscillations in power systems usually exist due to incompatible conditions and disturbances in the network. Meta-heuristics using search strategy are used to find near-optimal solutions. Typically, the implementation of this approach involves the utili MoreElectromechanical oscillations in power systems usually exist due to incompatible conditions and disturbances in the network. Meta-heuristics using search strategy are used to find near-optimal solutions. Typically, the implementation of this approach involves the utilization of a fitness function to assess the candidate solutions. In nature-inspired metaheuristics optimization algorithms, an analogy from nature is used to generate approximate solutions for practical optimization problems. This work presents a comprehensive investigation into various nature-inspired optimization algorithms, including ant colony optimization, genetic algorithm, and bat algorithm. The primary focus of this paper is to explore their efficacy in the coordinated design of Power System Stabilizers (PSS) and Flexible Alternating Current Transmission Systems (FACTS). The objective of this coordinated design is to improve energy system stability and mitigate power system oscillations. Finally, new directions are provided to researchers who work in the field of applications of nature-inspired optimization algorithms and coordination configuration of PSS and FACTS regulators. Manuscript profile -
Open Access Article
94 - Improving the Transient Stability of Power Systems Using STATCOM and Controlling it by Honey Bee Mating Optimization Algorithm
Ebadollah Amouzad Mahdiraji Seyed Mohammad Shariatmadar -
Open Access Article
95 - An Optimal Routing Protocol Using Multi-Objective Whale Optimization Algorithm for Wireless Sensor Networks
Seyed Reza Nabavi -
Open Access Article
96 - Optimum Cluster Head Selection with a Combination of Multi-Objective Grasshopper Optimization Algorithm and Harmony Search in Wireless Sensor Networks
Seyed Reza Nabavi Mehdi Najafi -
Open Access Article
97 - Optimal Placement and Scheduling of Switched Capacitor Banks Using Multi-Objective Hybrid Optimization Algorithm under Load Uncertainty Conditions
Ehsan Akbari -
Open Access Article
98 - A Swarm-based Scheduling Algorithm for Lifetime Improvement of Visual Sensor Networks
Mir Gholamreza Mortazavi Mirsaeid Hosseini Shirvani Arash Dana Mahmood FathyVisual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still im MoreVisual sensor networks (VSNs) apply directional sensors that can be configured only in one direction and also can be set in one of the possible observing ranges. In this battery-resource-limited environment, battery management and network lifetime expansion are still important challenges. The target coverage problem in such networks, in which all of the specified targets must be continuously observed and monitored by administrators is formulated as an integer linear programming problem (ILP) that is an NP-Hard problem. Although several approaches have been presented in the literature to solve the aforementioned problem, the majority of them suffer from getting stuck in the local trap and low exploration in search space. To address the issue, a discrete cuckoo-search optimization algorithm (DCSA) is extended to solve this combinatorial problem. The discrete operator of the proposed algorithm is designed in such a way that explore search space efficiently and lead to balancing in the local and global search process. The proposed algorithm was examined in different conducted scenarios. The returned results of simulations of numerous scenarios show the dominance of the proposed algorithm in comparison with other existing approaches in terms of network lifetime maximization. In other words, the proposed DCSA has 19.75% and 13.75% improvement in terms of network average lifetime expansion against HMNLAR and GA-based approaches respectively in all scenarios. Manuscript profile -
Open Access Article
99 - Load Frequency Control in Power Systems Using Multi Objective Genetic Algorithm & Fuzzy Sliding Mode Control
M. Khosraviani M. Jahanshahi M. Farahani A.R. Zare Bidaki -
Open Access Article
100 - Substation Expansion Planning Based on BFOA
H. Kiani Rad Z. Moravej -
Open Access Article
101 - Designing a credit portfolio optimization model in the banking industry using a meta-innovative algorithm
ali asghar tehrani poor Ebrahim Abbasi Hosein Didehkhani arash naderianThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk reali MoreThe purpose of this study is to design a credit portfolio optimization model in the banking industry using a meta-innovative algorithm. Risk is one of the basic concepts in financial markets that has a certain complexity. Due to the lack of a clear picture of risk realization, financial markets need risk control and management approaches. The present study is a descriptive survey in terms of data collection and applied in terms of purpose. The statistical population of this research includes all facility files of the last 10 years as well as the financial statements of Ansar Bank branches affiliated to Sepah Bank, which were selected by census method. The risk criteria used in the models are: fuzzy risk value, absolute value of fuzzy downward deviations and half entropy. Research models were implemented using multi-objective particle swarm optimization algorithm. The software used in conducting research is MATLAB software. The results show that the performance of the fuzzy risk-averaged model is better than the other two models in evaluating optimal portfolios. Therefore, the use of the above model in credit basket optimization is recommended. Manuscript profile -
Open Access Article
102 - Portfolio Optimization Using the Whale Algorithm with Expected Shortfall as the Measure of Risk
saeed fallahpour sepehr asefi sima fallahtafti MohammadReza BagherikazemabadPortfolio Selection is one of the most important decisions that institutional investors have to face. Markowitz was the first to introduce risk into the portfolio selection decision by introducing the Mean-Variance Model. This created one of the most importa MorePortfolio Selection is one of the most important decisions that institutional investors have to face. Markowitz was the first to introduce risk into the portfolio selection decision by introducing the Mean-Variance Model. This created one of the most important fields in finance, that is Portfolio Optimization and finding the efficient frontier. In the next researches, adding real world constraints to the model broadened this field. With increasing the number of assets or the constraints, Portfolio Optimization becomes an NP-hard problem which is impossible to solve with derivative-based methods, therefore, numerical and metaheuristic methods should be used for solving it. The aim of this research is optimizing portfolio using Whale optimization algorithm. This metaheuristic algorithm is inspired by the behavior of Whales and was introduced in 2016. This research implements the algorithm in the top 50 index in Tehran Stock Exchange and tries to find the efficient portfolio in this index. We also compare the performance of this method to two other metaheuristic algorithms and explain the advantages of the proposed method in portfolio optimization. Manuscript profile -
Open Access Article
103 - Assessing the credibility of auditors using artificial neural network
Asal Bakhshian Forough Heyrani Akram TaftiyanPurpose: The credibility of auditors is directly linked to the added value of confidence in communication between the auditee and their audience. Consequently, the credibility of auditors takes precedence due to the significance of audited financial statements in facili MorePurpose: The credibility of auditors is directly linked to the added value of confidence in communication between the auditee and their audience. Consequently, the credibility of auditors takes precedence due to the significance of audited financial statements in facilitating transactions in capital markets. This research aims to measure auditors' credibility using artificial neural networks. Methodology: Initially, research variables were identified using grounded theory, and factor analysis was employed to analyze research questions. The final model for measuring auditors' credibility was then presented, and MATLAB software was utilized to measure auditors' credibility. This research was conducted in the year 1401 (Solar Hijri calendar, equivalent to 2022-2023 in the Gregorian calendar). Findings: The results revealed that various factors influence the evaluation of auditors' credibility, including examining independence, acceptance or continuation of work, correspondence file, permanent file, understanding the unit under scrutiny and its environment (including internal controls), content tests, work planning, control and supervision, checklists and reports, the execution of duties by the second manager, overall assessment of audit files, the general status of the audit institution, human resource status within the audit institution, functions of the audit institution, compliance with auditing profession rules and regulations, and the external appearance of the audit institution. Conclusion: The weed optimization algorithm outperforms the particle swarm optimization algorithm in predicting auditors' credibility. Based on the findings of this study, stakeholders in the auditing profession, especially the Iranian Institute of Certified Accountants responsible for assessing auditors' credibility, should pay attention to the identified factors that experts and professionals believe to have an impact on evaluating auditors' credibility. Manuscript profile -
Open Access Article
104 - شبیه سازی حرکت پای انسان با مکانیزم یک درجه آزادی
دامون بختیاریان هادی همایی امین ملکی زاده مراد شهبازی تک آبینیاز به شبیه سازی الگوی حرکت پای انسان محققان و مهندسان را به سوی ارائه الگوهای متفاوت برای توصیف این حرکت کرده است. در این میان راه حل های بهینه از لحاظ مصرف انرژی و دقت و غیره از اهمیت بالایی برخوردارند. در این مقاله تلاش شده تا با طراحی یک مکانیزم کاملا دو بعدی شش می Moreنیاز به شبیه سازی الگوی حرکت پای انسان محققان و مهندسان را به سوی ارائه الگوهای متفاوت برای توصیف این حرکت کرده است. در این میان راه حل های بهینه از لحاظ مصرف انرژی و دقت و غیره از اهمیت بالایی برخوردارند. در این مقاله تلاش شده تا با طراحی یک مکانیزم کاملا دو بعدی شش میله ای با یک درجه آزادی به گونه ای که کمترین خطا را با حالت طبیعی راه رفتن پای انسان داشته باشد، راه حل جدیدی ارائه داده شود . ضمن اینکه یافته های این مقاله فرآیندی برای بهینه سازی مکانیزم های چندمیله ای ارائه می کند تا بتوان آن را در هر زمینه ی دیگری مانند ساخت پروتز پای انسان بکار برد. در اینجا از الگوریتم بهینه­سازی اجتماع ذرات استفاده شد. نتایج کار حاصل از بهینه سازی با داده های آزمایشگاهی پای انسان مقایسه شده است. نتایج نشان می دهد که مکانیزم شش میله ای پیشنهادی، ضمن بهینه کردن بسیاری از پارامترهای حرکتی به خوبی قادر به شبیه سازی حرکت پای انسان است. Manuscript profile -
Open Access Article
105 - A new design for PID controller by considering the operating points changes in Hydro-Turbine Connected to the equivalent network by using Invasive Weed Optimization (IWO) Algorithm
Navid Razmjooy Mohsen Khalilpour -
Open Access Article
106 - Robust Control of Power System Stabilizer Using World Cup Optimization Algorithm
Ali Madadi Navid Razmjooy Mehdi Ramezani
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