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دسترسی آزاد مقاله
1 - An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems
narges jafari Farhad Soleimanian GharehchopoghMetaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. T چکیده کاملMetaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance. پرونده مقاله -
دسترسی آزاد مقاله
2 - Improve Spam Detection in the Internet Using Feature Selection based on the Metahuristic Algorithms
Abdulbaghi Ghaderzadeh sahar Hosseinpanahi Sarkhel Taher kareemNowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining te چکیده کاملNowadays, spam is a major challenge regarding emails. Spam is a specific type of email that is sent to the network for malicious purposes. Spam plays an important role in stealing information and can include fake links to trick users. Machine learning and data mining techniques such as artificial neural networks are the most applicable methods to detect spam. The multi-layer artificial neural network needs to select the most important features as inputs to reduce the output error for accurate spam detection. In the proposed method, a smart method based on swarm intelligence algorithms is used for feature selection. In this study, a binary version of Emperor Penguin Optimizer (EPO) is used to select more appropriate features. The proposed method uses the selected features for learning and classification in the spam detection process. Experiments in the MATLAB environment on the Spambase dataset show that with the increase in population the error in spam detection in Emails will decrease about 14.61% and with the increase in feature space, it will decrease about 43.85% in the best situation. Experiments show that the proposed method has less error in detecting spam compare to other methods, multilayer artificial neural network, recursive neural network, support vector machine, Bayesian network, and whale optimization algorithm. Experiments show that the error of spam detection in the proposed approach is about 23.57% less than the whale optimization algorithm. Empirical results, obtained through simulations on the Spambase dataset, show our approach outperforms the other existing methods on precision value. پرونده مقاله -
دسترسی آزاد مقاله
3 - Opinion Leader’s Selection with Grey Wolf Optimizer Algorithm on Social Networks
S. Mohammad Aghdam F. Soleimanian Gharehchopogh M. MasdariOpinion leaders in social networks are beneficial and we will be able to use their empowerment and influence by identifying them. In this paper, we have chosen the opinion leaders with Grey Wolf Optimizer (GWO) algorithm. The results show, the number of actual opinion l چکیده کاملOpinion leaders in social networks are beneficial and we will be able to use their empowerment and influence by identifying them. In this paper, we have chosen the opinion leaders with Grey Wolf Optimizer (GWO) algorithm. The results show, the number of actual opinion leaders identified by this algorithm is significant and the advantage of the proposed method is compatibility with different criteria and providing sustainability results in different ways. پرونده مقاله -
دسترسی آزاد مقاله
4 - مکان یابی بهینه DG در یک شبکه نامتعادل با دو هدف جداگانه با استفاده از یک الگوریتم جدید
مجتبی جمعیتیدر این مقاله ، منابع تولید پراکنده (DG) در یک شبکه نامتعادل با دو تابع هدف متفاوت با استفاده از یک الگوریتم جدید ، محلی سازی شده اند. برای این منظور ، هزینه ها و توابع هدف پارامتریک برای این مشکل فرموله می شود. تکنیک جدیدی برای حل مشکل توزیع نامتعادل بار پیشنهاد شده است چکیده کاملدر این مقاله ، منابع تولید پراکنده (DG) در یک شبکه نامتعادل با دو تابع هدف متفاوت با استفاده از یک الگوریتم جدید ، محلی سازی شده اند. برای این منظور ، هزینه ها و توابع هدف پارامتریک برای این مشکل فرموله می شود. تکنیک جدیدی برای حل مشکل توزیع نامتعادل بار پیشنهاد شده است. الگوریتم بهینه سازی موتورهای جستجو گروهی (GSO) یکی از تکنیک های جدید ذرات هوشمند است که در این مقاله ، این الگوریتم بهبود یافته و نتایج مورد تجزیه و تحلیل قرار گرفته است. شبیه سازی ها بر روی دو شبکه نمونه واقعی در شمال غرب ایران و استاندارد IEEE انجام می شود. هر تابع هدف در یکی از شبکه ها آزمایش می شود. برای هر شبکه ، دو سناریوی قرارگیری پیشنهاد می شود: شمارش منابع DG و تعیین تعداد واحدهای DG. در فرایند مطالعه موردی ، نتایج الگوریتم بهبود یافته GSO با نتایج الگوریتم های ساده GSO و بهینه سازی ازدحام ذرات (PSO) مقایسه می شود. پرونده مقاله -
دسترسی آزاد مقاله
5 - Healthcare Districting Optimization Using Gray Wolf Optimizer and Ant Lion Optimizer Algorithms (case study: South Khorasan Healthcare System in Iran)
Hiwa Farughi Sobhan Mostafayi Jamal ArkatIn this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solut چکیده کاملIn this paper, the problem of population districting in the health system of South Khorasan province has been investigated in the form of an optimization problem. Now that the districting problem is considered as a strategic matter, it is vital to obtain efficient solutions in order to implement in the system. Therefore in this study two meta-heuristic algorithms, Ant Lion Optimizer (ALO) and Grey Wolf Optimizer (GWO), have been applied to solve the problem in the dimensions of the real world. The objective function of the problem is to maximize the population balance in each district. Problem constraints include unique assignment as well as non-existent allocation of abnormalities. Abnormal allocation means compactness, lack of contiguous, and absence of holes in the districts. According to the obtained results, GWO has a higher level of performance than the ALO. The results of this problem can be applied as a useful scientific tool for districting in other organizations and fields of application. پرونده مقاله -
دسترسی آزاد مقاله
6 - Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II
Fariba Maadanpour Safari Farhad Etebari Adel Pourghader ChobarIn this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a w چکیده کاملIn this research, a tri-objective mathematical model is proposed for the Transportation-Location-Routing problem. The model considers a three-echelon supply chain and aims to minimize total costs, maximize the minimum reliability of the traveled routes and establish a well-balanced set of routes. In order to solve the proposed model, four metaheuristic algorithms, including Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Water Cycle Algorithm (MOWCA), Multi-objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm- II (NSGA-II) are developed. The performance of the algorithms is evaluated by solving various test problems in small, medium, and large scale. Four performance measures, including Diversity, Hypervolume, Number of Non-dominated Solutions, and CPU-Time, are considered to evaluate the effectiveness of the algorithms. In the end, the superior algorithm is determined by Technique for Order of Preference by Similarity to Ideal Solution method. پرونده مقاله -
دسترسی آزاد مقاله
7 - ارائه مدل ﺷﺒﻴﻪﺳﺎز-ﺑﻬﻴﻨﻪﺳﺎز آبخوان دشت روانسر-سنجابی با تلفیق مدل GMS و اﻟﮕﻮرﻳﺘﻢ ﺑﻬﻴﻨﻪﺳﺎزی ﭼﻨﺪﻫﺪﻓﻪ ملهم از بیلیارد
عبدالغفور گری امیرپویا صراف حسن احمدیدر پژوهش حاضر، جهت ﺗﻌﻴﻴﻦ ﺳﻴﺎﺳﺖ ﺑﻬﻴﻨﻪ بهره‌برداری از آﺑﺨﻮان دشت روانسر-سنجابی در استان کرمانشاه، از یک مدل ﺷﺒﻴﻪﺳﺎز-ﺑﻬﻴﻨﻪﺳﺎز آب‌زﻳﺮزﻣﻴﻨﻲ استفاده شد. ﻣﺪل ارائه شده ترکیبی از ﻣﺪل ﺷﺒﻴﻪﺳﺎزی آﺑﺨﻮانGMS و اﻟﮕﻮرﻳﺘﻢ ﺑﻬﻴﻨﻪﺳﺎزی ﭼﻨﺪﻫﺪﻓﻪ ملهم از بیلیارد (MOBOA) در ﻣﺤﻴﻂ ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ متل چکیده کاملدر پژوهش حاضر، جهت ﺗﻌﻴﻴﻦ ﺳﻴﺎﺳﺖ ﺑﻬﻴﻨﻪ بهره‌برداری از آﺑﺨﻮان دشت روانسر-سنجابی در استان کرمانشاه، از یک مدل ﺷﺒﻴﻪﺳﺎز-ﺑﻬﻴﻨﻪﺳﺎز آب‌زﻳﺮزﻣﻴﻨﻲ استفاده شد. ﻣﺪل ارائه شده ترکیبی از ﻣﺪل ﺷﺒﻴﻪﺳﺎزی آﺑﺨﻮانGMS و اﻟﮕﻮرﻳﺘﻢ ﺑﻬﻴﻨﻪﺳﺎزی ﭼﻨﺪﻫﺪﻓﻪ ملهم از بیلیارد (MOBOA) در ﻣﺤﻴﻂ ﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲ متلب است. ابتدا، ﻣﺪل ﺟﻬﺖ ﺗﻌﻴﻴﻦ ﭘﺎراﻣﺘﺮﻫﺎی ﻫﻴﺪرودﻳﻨﺎﻣﻴﻜﻲ آﺑﺨﻮان واﺳﻨﺠﻲ و ﺻﺤﺖﺳﻨﺠﻲ شد. سپس با ﻛﻤﻴﻨﻪ ﻧﻤﻮدن ﺳﻪ ﺗﺎﺑﻊ هدف ﻛﻤﺒﻮد ﻧﺎﺷﻲ از ﻋﺪم ﺗﺄﻣﻴﻦ ﻧﻴﺎزﻫﺎ، اﻓﺖ تراز آب‌زیرزمینی و ﺷﺎﺧﺺ اﺻﻼح ﺷﺪه ﻛﻤﺒﻮد، ﻣﺪل مذکور ﺑﺮای ﻳﻚ دوره ﻳﻜﺴﺎﻟﻪ ﺑﺎ ١٢ دوره ﺗﻨﺶ ماهانه اﺟﺮا ﺷﺪ و ﺟﺒﻬﻪ پَرِتو ﺑﻬﻴﻨﻪ ﺣﺎﺻﻞ ﮔﺮدﻳﺪ. ﺑﻪﻋﻨﻮان ﻳﻚ ﻧﻤﻮﻧﻪ از ﺟﻮابﻫﺎی پَرِتو ﺑﻬﻴﻨﻪ ﻣﺤﺎﺳﺒﻪ ﺷﺪه، ﻣﺸﺎﻫﺪه ﺷﺪ در زﻣﺎﻧﻲ ﻛﻪ ﺳﻄﺢ اﻳﺴﺘﺎﺑﻲ ﺛﺎﺑﺖ ﺑﻤﺎﻧﺪ، ﻣﻘﺪار 7/١1 ﻣﻴﻠﻴﻮن ﻣﺘﺮ ﻣﻜﻌﺐ از ﻧﻴﺎزﻫﺎ ﺑﺎ ﻛﻤﺒﻮد ﻣﻮاﺟﻪ ﺷﺪه و ﺷﺎﺧﺺ اﺻﻼحﺷﺪه ﻛﻤﺒﻮد ﺑﺮاﺑﺮ 1٥/17 ﻣﻲﮔﺮدد. ﺟﻬﺖ ﺗﻌﻴﻴﻦ ﺑﻬﺘﺮﻳﻦ ﮔﺰﻳﻨﻪ استحصال از آب‎زیرزمینی ضروری است ضمن لحاظ نمودن ﻣﻌﻴﺎرﻫﺎی مختلف اﺟﺘﻤﺎﻋﻲ-اﻗﺘﺼﺎدی و حتی ﭘﻴﺎﻣﺪﻫﺎی زﻳﺴﺖ ﻣﺤﻴﻄﻲ ﺗﻮﺳﻂ ﻣﺴﺌﻮﻟﻴﻦ ﻣﺮﺑﻮﻃﻪ، ﺟﻮاب ﺑﻬﻴﻨﻪ ﻣﻨﺎﺳﺐ از ﻣﻴﺎن ﺳﺎﻳﺮ ﺟﻮابﻫﺎی ﺑﻬﻴﻨﻪ پَرِتو اﻧﺘﺨﺎب ﺷﺪه و ﻣﻘﺎدﻳﺮ استحصال ﻣﺘﻨﺎﻇﺮ ﺑﺎ ﺟﻮاب ﻣﻨﺘﺨﺐ ﺗﻌﻴﻴﻦ ﮔﺮدد. ﺑﺎ ﺗﺤﻠﻴﻞ ﻧﺘﺎﻳﺞ ﺣﺎﺻﻞ از ﺑﻪﻛﺎرﮔﻴﺮی ﺳﺎﺧﺘﺎر ﭘﻴﺸﻨﻬﺎدی ﻣﻲﺗﻮان درﻳﺎﻓﺖ ﻛﻪ روﻳﻜﺮد اراﺋﻪ ﺷﺪه از ﻛﺎراﻳﻲ ﺑﺴﻴﺎر ﺑﺎﻻﻳﻲ در ﺗﻌﻴﻴﻦ ﺳﻴﺎﺳﺖ ﺑﻬﻴﻨﻪ آﺑﺨﻮان برخوردار می‌باشد پرونده مقاله -
دسترسی آزاد مقاله
8 - Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems
غلامرضا عزیزیان فرید میارنعیمی محسن راشکی ناصر شابختیThis paper provides a novel meta-heuristic optimization algorithm. The behaviors of flying squirrels in the nature are the main inspiration of this research. These behaviors include flying from tree to tree and walking on the ground or on a tree branch to find food. The چکیده کاملThis paper provides a novel meta-heuristic optimization algorithm. The behaviors of flying squirrels in the nature are the main inspiration of this research. These behaviors include flying from tree to tree and walking on the ground or on a tree branch to find food. They also contact each other with chirp or squeak. This algorithm is named flying squirrel optimizer (FSO). Two main theories of motion were used for the simulation of flying and walking of the flying squirrels and they are Lévy flight and normal random walk. FSO is also benchmarked on twelve mathematical functions and the answers are compared with MFO, PSO, GSA, BA, FPA, SMS, and FA. The results show that FSO can provide good results when compared with these well-known meta-heuristics approaches. Five classical engineering problems and a real issue in the field of dam engineering were employed to challenge the FSO abilities in solving engineering design problems. The results also show that the proposed FSO algorithm can be used on a wide range of problems with unknown search spaces. پرونده مقاله -
دسترسی آزاد مقاله
9 - Optimal Allocation of Electric Parking and Distributed Generation in distribution system Based on Hybrid Water Cycle-Moth Flame Optimizer Algorithm
Mohammad Hadi Guity Navard Hamid LesaniIn this paper optimal allocation of electric vehicles parking and distributed generations (DGs) with objective of minimizing energy costs is proposed by using a hybrid water cycle-moth flame optimizer (WCMFO) algorithm. The purpose of the study is reduction the losses o چکیده کاملIn this paper optimal allocation of electric vehicles parking and distributed generations (DGs) with objective of minimizing energy costs is proposed by using a hybrid water cycle-moth flame optimizer (WCMFO) algorithm. The purpose of the study is reduction the losses of distribution system, improvement the voltage profile, minimization the distribution system voltage deviations, and reduction the energy received from the main feeder. The optimization problem is implemented on a 33 IEEE bus distribution system. In this study, due to the optimal combination of WCA and MFO methods, WCMFO method is used to solve the problem with high convergence rate. In this study, the proposed method is compared with WCA, MFO and particle swarm optimization (PSO) methods. The simulation results show superiority of the proposed hybrid method in achieving lower cost and high convergence rate. The results show that with the optimal use of electrical parking and also DGs, the system capacity can be released and the level of upstream system dependency can be reduced. It also reduced the amount of system energy not supplied. پرونده مقاله -
دسترسی آزاد مقاله
10 - Optimal Capacitor Placement in Radial Distribution Network Based on Power Loss Sensitivity Index Using Ant Lion Optimizer Considering Different Loading
Mehdi Bakhtiari Mehrdad Mallaki Nima MoaddabiIn this paper, the reactive resources placement including capacitor bank in radial distribution network is studied. The placement purpose is to reduce the cost of power loss, the cost of capacitor purchase and installation. The location and size of the capacitors in the چکیده کاملIn this paper, the reactive resources placement including capacitor bank in radial distribution network is studied. The placement purpose is to reduce the cost of power loss, the cost of capacitor purchase and installation. The location and size of the capacitors in the distribution network are determined using the intelligent ant lion optimizer (ALO) method, which is inspired by the hunting behavior of the ant lions. Based on the power loss sensitivity factor (LSF), candidate buses are selected for capacitor installation using the ALO. The proposed method is implemented ona 33-bus radial distribution networks. In this study, the effect of loading changes on the placement problem and distribution network characteristics including power losses, minimum voltage, voltage profile and net savings are evaluated. The results show that after optimal capacitor placement the characteristics of the distribution network includes active and reactive power loss are significantly reduced and also the network voltage profile is improved compared to former capacitor placement. The performance of the proposed method is compared to particle swarm optimization (PSO), teaching-learning based optimization (TLBO) and previous studies, which showed the superiority of the proposed method in achieving lower cost and greater net saving. پرونده مقاله
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