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Open Access Article
1 - Social Spider Optimization Algorithm in Multimodal Medical Image Registration
Zahra Hossein-Nejad Mehdi NasriMedical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images ta MoreMedical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images taken from the same scene by different modalities and different sensors. Intensity-based methods are widely used in multimodal medical image registration, these techniques register different modality images that have the same content by optimal transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, various optimization algorithms have been presented that the selection of appropriate optimization algorithms is very important in determining the optimal transformation parameter. The Social Spider Optimization (SSO) algorithm is one of the meta-heuristic methods that prevents premature convergence. In this paper, medical image registration technique is suggested based on the SSO algorithm. The Mutual Information (MI), Normalization of Mutual Information (NMI), and Sum of Squared Differences (SSD) are used separately as cost function (objective function) and the performance of each of these functions is checked in multimodal medical image registration. The simulation results on Brain Web data set affirm Manuscript profile -
Open Access Article
2 - 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
3 - Improve Spam Detection in the Internet Using Feature Selection based on the Metahuristic Algorithms
Abdulbaghi Ghaderzadeh sahar Hosseinpanahi Sarkhel Taher kareem -
Open Access Article
4 - An Overview of the Concepts, Classifications, and Methods of Population Initialization in Metaheuristic Algorithms
Mohammad Hassanzadeh farshid keynia -
Open Access Article
5 - Intelligent Resource Allocation in Fog Computing: A Learning Automata Approach
Alireza Enami Javad Akbari Torkestani -
Open Access Article
6 - Efficiency analysis of the meta-heuristic algorithms in portfolio optimization
Sina Shirtavani Mehdi Homayonfar Keyhan Azadi amir daneshvarThe most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in thi MoreThe most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in this research, the past performance of Tehran Stock Exchange companies is examined in past studies from 1390-1399, therefore, in terms of the research design, this research was post-event using Delphi and meta-analysis techniques. The statistical community of this research Academic researchers in the field of finance and active in the Tehran Stock Exchange, and the sampling method in this research was targeted with a volume of 30 people. The data collection tool was a researcher-made questionnaire. The method of collecting information was structured interview of researchers and review of the results of various studies in the field of determining the optimal stock portfolio in Tehran Stock Exchange. In order to analyze the data, Spss software version 23 and Laserl version 5.7 were used. The results showed that among meta-heuristic algorithms of genetic algorithm, ant colony and bee colony are the most suitable tools with the aim of not stopping at local optimal points and not premature convergence. Finally, after evaluating the appropriate algorithms, a comparison of the average risk and returns of the stock portfolio in genetic algorithms, ant colony and bee colony was done in the study unit, they showed that in terms of the criteria of reducing the risk of genetic and bee algorithms and in terms of increasing the return of the optimal portfolio Stock bee algorithm has worked more efficiently. Manuscript profile -
Open Access Article
7 - Designing Automatic Re-balancing Model Using Technical Analysis Concept of Divergence
S. M. Lale Sajjadi S. Hojjat Vakili S. Babak EbrahimiThe classical efficient market hypothesis states that it is not possible to beat the market by developing a strategy based on historical price series. In this paper we propose a profitable automatic trading system based on the divergence definition in relative strength MoreThe classical efficient market hypothesis states that it is not possible to beat the market by developing a strategy based on historical price series. In this paper we propose a profitable automatic trading system based on the divergence definition in relative strength index and using other technical analysis tools which presents empirical evidence confronting the classical efficient market hypothesis. In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies. An intraday database comprised of 59 symbols from NYSE in The time span 2010 to 2016 was employed. The whole sample is categorized over two sub-periods, training and widening its validity. By enjoying Meta-heuristic algorithms the rules in the first sub-period was improved. Then, in the second division the improved model was evaluated. The results indicates that this model improved predictability power and its performance is better than buy and hold and random strategies Manuscript profile -
Open Access Article
8 - New neighborhood approaches in memetic algorithm for customer type discovery
Hamed Sherafat Moula S.Hadi Yaghoubyan razieh malekhoseini Karamolah BagheriFard"Revenue management" systems are extensively utilized across various industries today. One of the primary pillars of revenue management lies in demand estimation, which predicts the demand for products and services. Understanding customers and their preferences forms th More"Revenue management" systems are extensively utilized across various industries today. One of the primary pillars of revenue management lies in demand estimation, which predicts the demand for products and services. Understanding customers and their preferences forms the cornerstone of demand estimation, and this understanding is acquired through solving the "customer type discovery" problem. Recently, this problem has been addressed using the "genetic" meta-heuristic method. In this research, we propose solving this problem utilizing the "memetic" meta-heuristic method, employing alternative approaches to identify the neighborhood. By evaluating real data from five hotels, we demonstrate that our method offers the first viable solution to the problem, resulting in a total of 10.5% fewer iterations compared to the "genetic" method. Manuscript profile -
Open Access Article
9 - A Trust-based Recommender System Using an Improved Particle Swarm Optimization Algorithm
Sajad Ahmadian Mohammad Hossein OlyaeeIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsit MoreIntroduction: Recommender systems are intelligent tools to help users find their desired information among a large number of choices based on their previous preferences in a way faster than search engines. One of the main challenges in recommender systems is the sparsity of the user-item rating matrix. This means that users mainly tend to express their opinions about a few items, leading to a large portion of the user-item rating matrix being empty. Trust-based recommender systems aim to alleviate the sparsity problem using trust relationships between users. Trust relationships can be used to calculate similarity values between users and determine the nearest neighbors set for the target user. However, the efficiency of trust-based recommender systems depends on the correct selection of neighboring users for the target user based on the similarity values between users. Method: In this paper, a novel trust-based recommender system is proposed based on an improved particle swarm optimization algorithm. To this end, first, the similarity values between users are calculated based on the user-item rating matrix and trust relationships. Then, the improved particle swarm optimization algorithm is used to optimally weight the neighboring users of the target user. The main purpose of this algorithm is to assign an optimal weight to each user in the nearest neighbor set of the target user to predict the unknown items accurately. After the optimal weighting of neighboring users, unknown ratings are predicted for the target user. Results: The proposed method is evaluated on a standard dataset in terms of mean absolute error, root mean square error, and rate coverage metrics. Experimental results demonstrate the high efficiency of the proposed method compared to other methods. Discussion: We use the genetic algorithms operators and chaos-based asexual reproduction optimization algorithm to improve the original version of the particle swarm optimization algorithm. The genetic algorithms operators increase the exploration mechanism of the particle swarm optimization algorithm, leading to a decline in the probability of tapping into local optima. Moreover, the chaos-based asexual reproduction optimization algorithm is applied to the best solution to further search the area around the best solution. Manuscript profile -
Open Access Article
10 - Determination of Optimal Parameters for Finite Plates with a Quasi-Square Hole
M Jafari M.H Bayati Chaleshtari E Ardalani -
Open Access Article
11 - Comparison of Portfolio Optimization for Investors at Different Levels of Investors' Risk Aversion in Tehran Stock Exchange with Meta-Heuristic Algorithms
Mohammad Hassan Fotros Idris Miri Ayob Miri -
Open Access Article
12 - Hybrid Multilayer Perceptron Neural Network with Grey Wolf Optimization for Predicting Stock Market Index
Meysam Doaei Seyed Ahmad Mirzaei Mohammad Rafigh -
Open Access Article
13 - Application of HS Meta-heuristic Algorithm in Designing a Mathematical Model for Forecasting P/E in the Panel Data Approach
Mozhgan Safa Hossein Panahian -
Open Access Article
14 - Presenting Evolutionary Model of Borrowing Sales using Collective Intelligence and Bird Flight Algorithm
Sadegh Hojjati Arash Naderian Majid Ashrafi Jamadordi Gorganli DojiThe purpose of this article is to present the evolutionary model of loan sales using collective intelligence and meta-heuristic algorithms (bird flight algorithm). In terms of method, this research is in the category of quantitative research, and in terms of purpose, it MoreThe purpose of this article is to present the evolutionary model of loan sales using collective intelligence and meta-heuristic algorithms (bird flight algorithm). In terms of method, this research is in the category of quantitative research, and in terms of purpose, it is included in the category of applied research. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is through library study and financial data of companies admitted to the stock exchange by referring to the financial statements and explanatory notes with the financial statements, and it has also been compiled using the Rahavard Novin software. Also, with the help of EViews 9 and MATLAB software, he presented a borrowing sales model, and in the next step, with the help of MATLAB software and the flight of bird's algorithm, he presented an evolutionary model of borrowing sales, in the end, by comparing the step-by-step regression model and the borrowing sales model. The findings showed that the borrowing sales model with the help of the bird flight algorithm has a higher efficiency. Manuscript profile -
Open Access Article
15 - A Mathematical Model to Optimize Cost, Time in The Three echelon Supply Chain in Post COVID 19 pandemic
Reza Ehtesham Rasi Jamal Mahmoodi Alireza Irajpoor -
Open Access Article
16 - Stochastic Facilities location Model by Using Stochastic Programming
Ali Gholinezhad Devin Saeed Fayyaz Reza Sadeghi -
Open Access Article
17 - The indifference points in multi-criteria decision problems (case stady Evalution Supplyers in Zanjan Province Water and Wastewater Company)
Reza Radfar ARSHAD FARAHMANDIAN Mohammad Ali Afshar KazemiDecisions on the process of assessment and selection of suppliers should be made by examining all possible options, otherwise the organization will encounter many problems during the implementation and implementation phases.The purpose of the present study was to determ MoreDecisions on the process of assessment and selection of suppliers should be made by examining all possible options, otherwise the organization will encounter many problems during the implementation and implementation phases.The purpose of the present study was to determine the indifference points of assessors of the water and wastewater company in Zanjan province.The method of this study was descriptive. The data of this study is related to supplier assessment of one of the projects of the city water and wastewater company Zanjan province.The data was collected based on the views of 10 experts with at least a bachelor's degree and at least 5 years of work experience in the company based on the "supplier assessment form".The data has been analyzed using the 2014 version of MATLAB software.A total of 10 cases of matrix matched with the initial decision matrix are identified and generated separately for each method.TOPSIS-GA = 2 and TOPSIS-PSO = 3 and AHP-GA = 2 and AHP-PSO = 3. A total of 10 cases of matrix matched with the initial decision matrix are identified and generated separately for each method. Manuscript profile -
Open Access Article
18 - Comparing and Ranking of Meta-Heuristic Algorithms Using Group Decision Making Methods
Hojatollah Rajabi Moshtaghi Abbas Toloie Eshlaghy Mohammad Reza MotadelIn recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear and NP-hard problems have dramatically increased, while new algorithms have constantly being introduced. In this research, with the aim of ranking meta-heuristic algorithm MoreIn recent years, meta-heuristic algorithms and their application in solving complicated, nonlinear and NP-hard problems have dramatically increased, while new algorithms have constantly being introduced. In this research, with the aim of ranking meta-heuristic algorithms, using group decision making techniques (different from other research in this field), 5 algorithms including: GA, PSO, ABC, SFLA and ICA by 15 standard test functions, and considering 2 attribute: "mean of answers" and "run time", have been compared. Then they are ranked by 3 group decision making methods including: "Cook and Seiford", "Condorcet" and "Dodgson". In addition, as in ranking by "Condorcet" and "Dodgson" methods, sometimes some options posit the same rank, therefore, in this study; we presented a proposal to overcome the limitation. Then the algorithms with these proposed methods were ranked. Finally, the overall ranking is done using an allocation model our results show that the overall ranking is as follows, respectively: PSO, ICA, GA, ABC and SFLA. Manuscript profile -
Open Access Article
19 - Designing an Optimal Recycling Model in a Four-Level Closed-Loop Supply Chain by Queuing Theory and Robust Planning (Case Study: Paper Industry)
Mahdi Alizadeh Beromi Mohammad Ali Afshar Kazemi Mohammadali Keramati Abbass Toloie AshlaghiIn recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously MoreIn recent years, the growing industrial and economic competition, environmental concerns, and governmental pressures on manufacturers regarding waste management have underscored the significance of designing a reverse supply chain and closed-loop network. Simultaneously, the potential for profit arising from product recycling has further emphasized the importance of these systems. This research focuses on developing a four-stage closed-loop network model for the supply chain, taking into account the uncertainty of product recycling rates. The primary objective of this study is to provide an integer linear programming model aimed at minimizing supply chain costs and customer service time under uncertain conditions. The supply chain model is designed by integrating queuing theory and product recycling system optimization. A critical aspect of this research involves modeling the uncertainty in the return rate of consumer products in the closed-loop supply chain, with the aim of developing a robust approach to address this issue. Additionally, the performance of the proposed model in the paper industry is evaluated, and a sensitivity analysis is conducted with respect to the decision variables using two metaheuristic algorithms: the Multiple Objective Harmony Search and the Non-dominated Sorting Genetic Algorithm. Manuscript profile -
Open Access Article
20 - Developing Dynamic Facility Layout Problem with Single-Solution and Population-Based Metaheuristics Methods
Mohammad Mahdi Karampour Mostafa Hajiaghaei-KeshteliIn modern societies, production centers should be able to supply the variety of demand and type of productions correctly, exactly, clearly and straightly. So, this study aims to present the dynamic facility layout problem (DFLP) in order to minimize the total cost of th MoreIn modern societies, production centers should be able to supply the variety of demand and type of productions correctly, exactly, clearly and straightly. So, this study aims to present the dynamic facility layout problem (DFLP) in order to minimize the total cost of the facilities and costumes. Design an optimal plan leads to reduce the production process and cost of inventory. Transportation cost plays a key-role to specify the performance designing of part systems. This content displays the Dynamic Facility Location Problem (DFLP) in this paper. In order to handle the proposed problem, five metaheuristic methods are introduced: Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search (TS), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) are tackled to solve the problem. Taguchi approach is considered to select the proper values for presented algorithms. The results explain that proposed methods are provided the optimal design for the dynamic facility location problem. As a result, ICA and PSO reach the most performance output in the comparison of other algorithms with assumed conditions in the problem. Manuscript profile -
Open Access Article
21 - Power and weight optimization of spur gears using metaheuristics and finite element method
Mohammad Sadeghi Ali SadollahGearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. In this paper, a spur gear model is optimized aiming to maximize its transmission power and minimize its weight. Several des MoreGearing is one of the most efficient methods of transmitting power from a source to its application with or without change of speed or direction. In this paper, a spur gear model is optimized aiming to maximize its transmission power and minimize its weight. Several design variables named as transmitted power, number of pinion teeth, modules, and thickness of gears have been considered during optimization process. For the sake of optimization, two developed metaheuristics named as water cycle and neural net-work algorithms have been examined using MATLAB programming language platform. Besides, obtained optimization results have been validated and analyzed using well-known commercial computer aided engineering software ANSYS. Based on the ob-tained optimization results, optimum design has been found using optimizers and in terms of engineering analysis good agreement has been observed between the applied finite element approach. Manuscript profile -
Open Access Article
22 - A Review of Feature Selection Method Based on Optimization Algorithms
Zohre Sadeghian Ebrahim Akbari Hossein Nematzadeh Homayun Motameni -
Open Access Article
23 - Indifferent points in the multicriteria decision making problems
Arshad Farahmandian Reza Radfar MohammadAli Afshar KazemiThe process of evaluating and selecting suppliers should be done by examining all possible options and scenarios for each contractor, otherwise the organization will face a lot of difficulties in the implementation and implementation phase of the commitments. The purpos MoreThe process of evaluating and selecting suppliers should be done by examining all possible options and scenarios for each contractor, otherwise the organization will face a lot of difficulties in the implementation and implementation phase of the commitments. The purpose of this study was to determine the indifference points of the suppliers of gas companies in Zanjan province.This study is descriptive. The data of this study is related to the evaluation of suppliers of Zanjan province gas company projects. The data was collected based on the views of 10 experts with at least a bachelor's degree and at least 5 years of work experience in the company based on the "supplier assessment form". The data has been analyzed using the 2014 version of MATLAB software.A total of 6 items of matrix matched with the initial decision matrix are identified and generated for each method.TOPSIS-GA = 2 and TOPSIS-PSO = 3 and AHP-GA = 2 and AHP-PSO = 3. From the results of the ranking of options, the third-party contractor has ranked first among the other options. Depending on the indeterminate matrices identified, different scenarios are set for the third contractor. Considering the company's budget and expectations that are more in line with the fourth indifference point (OUT PUT-5-AHP-PSO), the third contractor is being asked to strengthen his capabilities so that he will succeed in the contract with his chance for future cooperation. Increase. Manuscript profile -
Open Access Article
24 - Meta-heuristic Algorithms for an Integrated Production-Distribution Planning Problem in a Multi-Objective Supply Chain
Abolfazl Kazemi Fatemeh Kangi Maghsoud Amiri -
Open Access Article
25 - Optimization of Multi-period Three-echelon Citrus Supply Chain Problem
Navid Sahebjamnia Fariba Goodarzian Mostafa Hajiaghaei-Keshteli -
Open Access Article
26 - Solving the Fixed Charge Transportation Problem by New Heuristic Approach
Komeil Yousefi Ahmad J. Afshari Mostafa Hajiaghaei-Keshteli -
Open Access Article
27 - Bi-objective Optimization of a Multi-product multi-period Fuzzy Possibilistic Capacitated Hub Covering Problem: NSGA-II and NRGA Solutions
Zahra Rajabi Soroush Avakh Darestani -
Open Access Article
28 - Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms
Surur Yaghobi Harzandi Amir Abbas Najafi -
Open Access Article
29 - Developing a Fuzzy Green Supply Chain Management Problem Considering Location Allocation Routing Problem: Hybrid Meta-Heuristic Approach
Behzad Aghaei Fishani Ali Mahmoodirad Sadegh Niroomand Mohammad Fallah -
Open Access Article
30 - Scheduling on flexible flow shop with cost-related objective function considering outsourcing options
Mojtaba Enayati Ebrahim Asadi-Gangraj Mohammad Mahdi Paydar -
Open Access Article
31 - Design of a Mathematical Model for Logistic Network in a Multi-Stage Multi-Product Supply Chain Network and Developing a Metaheuristic Algorithm
Esmaeil Mehdizadeh Fariborz Afrabandpei -
Open Access Article
32 - Indifferent Points in The Multicriteria Decision Making Problems (A Case Study of Suppliers’ Evaluation in Zanjan Province Gas Company)
Arshad Farahmandian reza radfar mohammad ali afshar kazemi -
Open Access Article
33 - Solving Group Scheduling Problem in No-wait Flow Shop with Sequence Dependent Setup Times
Abolfazl Adressi Reza Bashirzadeh Vahid Azizi Saeed Tasouji Hassanpour -
Open Access Article
34 - Investing Neural Network Trianing with Metaheuristic Algorithms in order to Prediction of Iran Stock Index
Seyed Ahmad Mirzaei Zakiyeh Nikdel Zahra NikdelPrediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock pric MorePrediction and analysis of stock market movements are an important topic for researchers, traders and have got an important role in today’s economy. Variety in policies, such as government policies and economic policies affect the stock market and cause stock price changes. The predicting stock price movement on a daily basis due to the non-linear and chaotic stock price movements is a difficult task. There are several ways for predicting in stock market. Artificial intelligence techniques have been widely used to predict data with nonlinear and chaotic structure. One of these techniques is neural network. If neural network is trained correctly, then it has minimum error in predicting. In this research, we will train the multi layer perceptron neural network with 8 meta heuristics algorithms and we predict Tehran Exchange Dividend Price Index (TEDPIX). The Results show that grey wolf optimization has the minimum error in training of neural network. Manuscript profile -
Open Access Article
35 - Fuzzy Portfolio Optimization Using Credibility Theory: Multi-Objective Evolutionary Optimization Algorithms
MariehAlsadat MirAboalhassani Farzad Movahedi Sobhani Emran Mohammadi -
Open Access Article
36 - مروری بر الگوریتم های فراابتکاری و تحلیل پوششی داده ها
Mohsen Vaez-ghasemi Zohreh Moghaddas Hamid Askari Feloora Valizadehامروزه بسیاری از فعالیتها از کسب و کار گرفته تا طراحیهای مهندسی، مسیریابی در اینترنت و حتی مسیریابی کامیونهای حمل مواد غذایی و غیره نیازمند برنامه ریزی و بهینه سازی هستند. تعداد زیادی از این مسائل راه حل قطعی نداشته و یا به راحتی قابل حل نیستند و برای حلشان الگوریتمهایی Moreامروزه بسیاری از فعالیتها از کسب و کار گرفته تا طراحیهای مهندسی، مسیریابی در اینترنت و حتی مسیریابی کامیونهای حمل مواد غذایی و غیره نیازمند برنامه ریزی و بهینه سازی هستند. تعداد زیادی از این مسائل راه حل قطعی نداشته و یا به راحتی قابل حل نیستند و برای حلشان الگوریتمهایی با الهام از طبیعت و بر مبنای هوش ذرات، سیستمهای زیستی، فیزیکی، شیمیایی وحتی جوامع انسانی توسعه داده میشوند که نامگذاری بسیاری از آنها نیز بر اساس منبع الهام گیری اولیه است؛ یک الگوریتم بهینه سازی فراابتکاری یک روش ابتکاری است که میتواند باتغییرهایی کم برای مسائل مخلتف بهینه سازی به کاررود الگوریتم های فراابتکاری بطور قابل ملاحظه ای توانایی یافتن جوابهای با کیفیت بالا را برای مسائل بهینه سازی سخت افزایش میدهد . در این مقاله به بررسی و مرور کاربرد انواع الگوریتم های فراابتکاری و تحلیل پوششی داده ها در مسائل بهینه سازی موجود در مجموعه مقالات منتشر شده در چند سال گذشته پرداخته شده است. آنچه که در این مقاله آمده است توضیحاتی درباره کاربرد انواع الگوریتم های فراابتکاری در تحلیل پوششی داده ها ،بیان کاربرد و حوزه فعالیتشان و همپوشانی و استفاده تلفیقی از این دو روش قدرتمند برای دستیابی به جواب بهینه است Manuscript profile -
Open Access Article
37 - Communication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 Topology
Sara Farzai Mirsaeid Hosseini Shirvani Mohsen Rabbani -
Open Access Article
38 - A Meta-heuristic Approach to CVRP Problem: Local Search Optimization Based on GA and Ant Colony
Arash Mazidi Mostafa Fakhrahmad Mohammadhadi Sadreddini -
Open Access Article
39 - Robust Scheduling and Planning of Operating Rooms and Sterilization Unit with Emergency and Elective Patients: Two Metaheuristic Algorithms
Fatemeh Arjmandi Parvaneh SamoueiGreat attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusab MoreGreat attention should be paid to planning and scheduling surgeries in the operating room which is the most sensitive ward in the health context in terms of cost and specific sensitivity due to its association with the life and death of individuals. In this case, reusable sterile equipment and devices are crucial issues because the hospital or nosocomial infections result from insufficient sterilization of these instruments. Therefore, sterilization of reusable medical devices is a necessity in the operating room to prevent possible infections. This study solves the integrated operating rooms and sterile section planning problem to minimize the total costs of sterilization, surgery postponement, and performance. This study also minimizes the completion time of surgery considering nondeterministic operating times and emergency-elective patients. In the real world, surgery time may be nondeterministic based on the conditions of the patient, surgeon, equipment, and instruments; hence, it is valuable to find a robust solution for planning under such circumstances. After presenting a bi-objective mathematical model for this problem, an improved epsilon constraint method was used to solve problems with small dimensions, and two metaheuristics NSGA-II and NRGA were developed for large dimensions regarding NP-hard problems. These two algorithms were analysed in terms of five indicators. The results indicated the superiority of the NSGA-II algorithm over NRGA to solve such problems. Manuscript profile -
Open Access Article
40 - Fuzzy modeling of allocation of financial resources of sustainable projects and Solving with GSSA algorithm
Mohsen Amini Khouzani Alireza Sadeghi Amir Daneshvar Adel Pourghader ChobarThe problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this i MoreThe problem of allocation of financial resources in projects is one of the most important problems of mathematical optimization. Incorrect allocation of financial resources can lead to project failure, increased costs, and reduced profitability. The importance of this issue has led to the modeling of a financial resource allocation problem for sustainable projects under uncertainty in this article. A fuzzy programming method was used to control model parameters and GSSA, GA, and SSA algorithms were used to solve the model. In the mathematical model, the goal was to optimize the objective function consisting of predicted return, investment risk, and project sustainability. Mathematical calculation results showed that meta-heuristic algorithms have high efficiency in achieving optimal solutions in a short time. so that the average time to solve them was less than 10 seconds. Also, the calculation results showed that increasing the uncertainty rate leads to increasing the value of the objective function and creating a distance from the optimal point. This is due to increasing costs and decreasing profits in sustainable projects. Finally, usage the TOPSIS method, the ranking of solving algorithms was done, and the GSSA algorithm was the most efficient algorithm among other algorithms with a desirability weight of 0.846. Manuscript profile -
Open Access Article
41 - Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm
Hamed Jafari Nasser Salmasi -
Open Access Article
42 - A modified elite ACO based avoiding premature convergence for travelling salesmen problem
M Yousefikhoshbakht E Mahmoodabadi M Sedighpour -
Open Access Article
43 - Presenting the Evolutionary Model of Short Selling Using Collective Intelligence and Ant Colony Algorithm
Sadegh Hojjati Arash Naderian Majid Ashrafi Jamadverdi Gorganli DojiThe purpose of this research is to present the evolutionary model of short selling using collective intelligence and an ant colony algorithm. In terms of method, this research is in the category of quantitative research, and the purpose of the study is practical. The st MoreThe purpose of this research is to present the evolutionary model of short selling using collective intelligence and an ant colony algorithm. In terms of method, this research is in the category of quantitative research, and the purpose of the study is practical. The statistical population includes all active companies admitted to the Tehran Stock Exchange. This research has been investigated between 2011 and 2019 for active companies admitted to the Tehran Stock Exchange. The method of data collection is library-type and uses foreign and domestic articles and financial data of companies admitted to the stock exchange, which has been compiled by referring to financial statements and explanatory notes using the new Rahvard Novin software. In the following, we have presented the short-selling model by using EViews 9 software and MATLAB, and then using MATLAB software and the ant colony algorithm, we have presented the evolutionary model of short selling. In the end, by comparing the step-by-step regression model and the borrowed sales model (ant colony algorithm), he presented a model that is more efficient than other models. The result of the research indicates that the short-selling model with the help of the ant colony algorithm has a higher efficiency. Manuscript profile -
Open Access Article
44 - The Effect of Demand-Response Program and Distributed Generation Resources on Optimal Establishment of Electric Vehicle Charging/Discharging Stations Using a Triple Optimization Algorithm
Monireh Ahmadi Seyed Hossein Hosseini Murteza Farsadi -
Open Access Article
45 - Intelligent Hybrid Heuristic-Metaheuristic Algorithm for Lifetime Extension in Wireless Body Area Networks
Pouya Aryai Ahmad Khademzadeh Somayyeh Jafarali Jassbi Mehdi Hosseinzadeh -
Open Access Article
46 - Stock portfolio optimization of companies listed on the Tehran Stock Exchange based on a combination of two-level ensemble machine learning methods and multi-objective meta-innovative algorithms based on market timing approach
sanaz faridi amir daneshvar Mahdi Madanchi Zaj Shadi ShahverdianiIn this article, using the market timing approach and homogeneous and inhomogeneous collective learning methods, the purchase, maintenance and sales signal and market forecast are presented based on the basic characteristics, technical characteristics and time series of MoreIn this article, using the market timing approach and homogeneous and inhomogeneous collective learning methods, the purchase, maintenance and sales signal and market forecast are presented based on the basic characteristics, technical characteristics and time series of returns of each company in the 100 days leading to the current day. . Based on this, 208 companies were selected as active companies between 1390 and 1399 To teach data by two-level ensemble learning machine (HHEL) and market trend forecasting based on market timing strategy, use data from 5 years 1390 to 1394 and to test the data as stock portfolio optimization based on stock portfolio maximization and risk minimization. The investment portfolio uses MOPSO and NSGA II algorithms and is compared with the obtained investment portfolio with the buy and hold strategy. The results showed that the MOPSO algorithm achieved the highest stock portfolio yield with 96.437% compared to the NSGA II algorithm with a yield of 91.157% and the same investment method with a yield of 13.058%. Also, the portfolio risk in NSGA II algorithm was much lower than the portfolio risk in MOPSO algorithm with 0.792% and 1.367%, respectively Manuscript profile -
Open Access Article
47 - Smart operating system based on technical parameters optimized with firefly algorithm
Fatemeh Asiaei Taheri Gholamreza zomorodian Mirfeiz FallahshamsThe main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done clo MoreThe main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches. Manuscript profile -
Open Access Article
48 - Optimization of technical indicators’ parameters for intraday data using optics – inspired optimization (OIO): a case study of Tehran stock exchange
Mohammad Ali Rastegar Farah AshuriIn this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) al MoreIn this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) algorithm. In the proposed model the technical indicators’ optimized parameters are obtained using both COIO and genetic algorithms with the aim of maximization of modified Sharpe ratio. The presented paper uses stock intra-day prices as input data and considers the transaction costs. The designed strategy is compared against several other approaches including: using the indicators’ default parameters, buy and hold strategy and optimization using genetic algorithm, for both daily and intra-day prices and due to a greater modified Sharpe ratio for the proposed model, its superiority is shown in all cases. Moreover, in a comparison based on end- of- period returns, it is shown that without considering the transaction costs the results of the intra-day data beats the results of the daily data while no superiority is observed when considering the transaction costs. So reducing the transaction costs is recommended to motivate traders to trade on an intra-day basis. Manuscript profile -
Open Access Article
49 - Tehran Stock Exchange Overal Index Prediction using Combined Approach of Metaheuristic Algorithms, Artificial Intelligence and Parametric Mother Wavelet
Alireza Saranj Madjid Ghods reza tehraniUnderstanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which ha MoreUnderstanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which have a better performance than the traditional models. Here a hybrid model of neural network and wavelet transform is proposed in which genetic algorithm has been used to improve the performance of wavelet transform in optimizing the wavelet function. Daily stock exchange rates of TSE from April 21, 2012 to April 19, 2017 are used to develop a prediction model. The results show that it is possible to find a wavelet basis, which will be appropriate to the intrinsic characteristics of time series for prediction and the prediction error in this model is reduced comparing to the neural network and hybrid neural network and wavelet models. Manuscript profile -
Open Access Article
50 - Meta-heuristic Algorithms for the Tower Crane Planning on the Site
Roya Amiri Javad Majrouhi Sardroud Vahid Momenaei KermaniResearch projects show that the desire for intelligent approaches to decision-making at various stages of the construction industry is increasing. Site layout planning is one of the important decision-making processes in the early stages of construction projects, where MoreResearch projects show that the desire for intelligent approaches to decision-making at various stages of the construction industry is increasing. Site layout planning is one of the important decision-making processes in the early stages of construction projects, where the location of facilities must be determined within the site. Tower crane is considered as one of the vital and expensive facilities in construction sites. Proper locating of tower crane has a significant impact on the quality, productivity, safety, cost and time of the project. In choosing the location of the tower crane, there are several criteria, including the largest lifting radius and capacity of the tower crane, the type of soil on site, the soil-bearing capacity and the material supply points. Therefore, due to the influence of many factors, tower crane planning is a complex NP-hard optimization problem, which cannot be solved through exact mathematical algorithms as the number of parameters and variables increases. Therefore, it is necessary to define the problem as an optimization problem and integrate it with mathematical modeling to reach the optimal solution. Solving such problems is usually done through metaheuristic algorithms, which belong to the category of approximate algorithms. This study provides a comprehensive review on tower crane planning problem on construction sites using mathematical modeling and metaheuristic algorithms. Based on the findings of this study, research gaps are identified in this field. Therefore, suggestions for future works have been presented in order to solve the shortcomings, which can be the subject of various research articles. Manuscript profile -
Open Access Article
51 - Controller Placement in SDN using Honey Badger Algorithm
Kambiz Majidzadeh mahnaz khojand mohammad masderi yousef farhangSoftware-defined networking (SDN) is a network structure where the control and data planes are separated. In traditional SDN, a single controller was in charge of control management, but this architecture had several constraints. To address these constraints, it is advi MoreSoftware-defined networking (SDN) is a network structure where the control and data planes are separated. In traditional SDN, a single controller was in charge of control management, but this architecture had several constraints. To address these constraints, it is advisable to incorporate multiple controllers in the network. Selecting the number of controllers and connecting switches to them is known as the controller placement problem (CPP). CPP is a key hurdle in enhancing SDNs. In this paper a meta-heuristic algorithm called Honey Badger Algorithm (HBA), is used to determine the optimal alignment between switches and controllers. HBA is modified using genetic operators (GHBA). The assessments are conducted with a diverse range of controllers on four prominent software-defined networks sourced from the Internet Topology Zoo and are compared to numerous algorithms in this field. It is noted that GHBA outperforms other competing algorithms in terms of end-to-end delay and energy consumption. Manuscript profile
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