Duality evolution: an efficient approach to constraint handling in multi-objective particle swarm optimization
This paper proposes an efficient approach for constraint handling in multi-objective particle swarm optimization. The particles population is divided into two non-overlapping populations, named infeasible population and feasible population. The evolution process in each population is done independen...
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Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2017-12, Vol.21 (24), p.7251-7267 |
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description | This paper proposes an efficient approach for constraint handling in multi-objective particle swarm optimization. The particles population is divided into two non-overlapping populations, named infeasible population and feasible population. The evolution process in each population is done independent of the other one. The infeasible particles are evolved in the constraint space toward feasibility. During evolution process, if an infeasible particle becomes a feasible one, it migrates to feasible population. In a parallel process, the particles in feasible population are evolved in the objective space toward Pareto optimality. At each generation of multi-objective particle swarm optimization, a leader should be assigned to each particle to move toward it. In the proposed method, a different leader selection algorithm is proposed for each population. For feasible population, the leader is selected using a priority-based method in three levels and for infeasible population, a leader replacement method integrated by an elitism-based method is proposed. The proposed approach is tested on several constrained multi-objective optimization benchmark problems, and its results are compared with two popular state-of-the-art constraint handling multi-objective algorithms. The experimental results indicate that the proposed algorithm is highly competitive in solving the benchmark problems. |
doi_str_mv | 10.1007/s00500-016-2422-5 |
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The particles population is divided into two non-overlapping populations, named infeasible population and feasible population. The evolution process in each population is done independent of the other one. The infeasible particles are evolved in the constraint space toward feasibility. During evolution process, if an infeasible particle becomes a feasible one, it migrates to feasible population. In a parallel process, the particles in feasible population are evolved in the objective space toward Pareto optimality. At each generation of multi-objective particle swarm optimization, a leader should be assigned to each particle to move toward it. In the proposed method, a different leader selection algorithm is proposed for each population. For feasible population, the leader is selected using a priority-based method in three levels and for infeasible population, a leader replacement method integrated by an elitism-based method is proposed. The proposed approach is tested on several constrained multi-objective optimization benchmark problems, and its results are compared with two popular state-of-the-art constraint handling multi-objective algorithms. 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The particles population is divided into two non-overlapping populations, named infeasible population and feasible population. The evolution process in each population is done independent of the other one. The infeasible particles are evolved in the constraint space toward feasibility. During evolution process, if an infeasible particle becomes a feasible one, it migrates to feasible population. In a parallel process, the particles in feasible population are evolved in the objective space toward Pareto optimality. At each generation of multi-objective particle swarm optimization, a leader should be assigned to each particle to move toward it. In the proposed method, a different leader selection algorithm is proposed for each population. For feasible population, the leader is selected using a priority-based method in three levels and for infeasible population, a leader replacement method integrated by an elitism-based method is proposed. The proposed approach is tested on several constrained multi-objective optimization benchmark problems, and its results are compared with two popular state-of-the-art constraint handling multi-objective algorithms. The experimental results indicate that the proposed algorithm is highly competitive in solving the benchmark problems.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Benchmarks</subject><subject>Business metrics</subject><subject>Computational Intelligence</subject><subject>Constraints</subject><subject>Control</subject><subject>Elitism</subject><subject>Engineering</subject><subject>Evolution</subject><subject>Feasibility</subject><subject>Foundations</subject><subject>Genetic algorithms</subject><subject>Handling</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methods</subject><subject>Multiple objective analysis</subject><subject>Optimization</subject><subject>Pareto optimization</subject><subject>Particle swarm optimization</subject><subject>Robotics</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1UMtOAyEUJUYTa_UD3JG4RhmYGYo7U59JEze6JpS509LMwAhMTf16qTVx5eo-ch73HoQuC3pdUCpuIqUVpYQWNWElY6Q6QpOi5JyIUsjjn54RUZf8FJ3FuKGUFaLiE-TuR93ZtMOw9d2YrHe3WDsMbWuNBZewHobgtVnj5LHxLqagbV6vtWs661bYOtyPXbLELzdgkt0CHnRI1nSA46cOPfZDsr390nvxc3TS6i7CxW-dovfHh7f5M1m8Pr3M7xbE8LpMpGoo5XXTlIJJYKYGaYTkyzwZU3NqhOFSwgwErVkjIX8Nhs94A-2sbo1p-BRdHXTz8R8jxKQ2fgwuWyomCyEZFVxmVHFAmeBjDNCqIdheh50qqNrHqg6xqmyg9rGqKnPYgRMz1q0g_Cn_T_oGrnJ84Q</recordid><startdate>20171201</startdate><enddate>20171201</enddate><creator>Ebrahim Sorkhabi, Amin</creator><creator>Deljavan Amiri, Mehran</creator><creator>Khanteymoori, Ali Reza</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20171201</creationdate><title>Duality evolution: an efficient approach to constraint handling in multi-objective particle swarm optimization</title><author>Ebrahim Sorkhabi, Amin ; Deljavan Amiri, Mehran ; Khanteymoori, Ali Reza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-5d0036dd4729e2c6e9c793b729cc630c7c399e8e7062d9e016ec383def86fccd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Benchmarks</topic><topic>Business metrics</topic><topic>Computational Intelligence</topic><topic>Constraints</topic><topic>Control</topic><topic>Elitism</topic><topic>Engineering</topic><topic>Evolution</topic><topic>Feasibility</topic><topic>Foundations</topic><topic>Genetic algorithms</topic><topic>Handling</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methods</topic><topic>Multiple objective analysis</topic><topic>Optimization</topic><topic>Pareto optimization</topic><topic>Particle swarm optimization</topic><topic>Robotics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ebrahim Sorkhabi, Amin</creatorcontrib><creatorcontrib>Deljavan Amiri, Mehran</creatorcontrib><creatorcontrib>Khanteymoori, Ali Reza</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ebrahim Sorkhabi, Amin</au><au>Deljavan Amiri, Mehran</au><au>Khanteymoori, Ali Reza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Duality evolution: an efficient approach to constraint handling in multi-objective particle swarm optimization</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2017-12-01</date><risdate>2017</risdate><volume>21</volume><issue>24</issue><spage>7251</spage><epage>7267</epage><pages>7251-7267</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>This paper proposes an efficient approach for constraint handling in multi-objective particle swarm optimization. The particles population is divided into two non-overlapping populations, named infeasible population and feasible population. The evolution process in each population is done independent of the other one. The infeasible particles are evolved in the constraint space toward feasibility. During evolution process, if an infeasible particle becomes a feasible one, it migrates to feasible population. In a parallel process, the particles in feasible population are evolved in the objective space toward Pareto optimality. At each generation of multi-objective particle swarm optimization, a leader should be assigned to each particle to move toward it. In the proposed method, a different leader selection algorithm is proposed for each population. For feasible population, the leader is selected using a priority-based method in three levels and for infeasible population, a leader replacement method integrated by an elitism-based method is proposed. 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subjects | Algorithms Artificial Intelligence Benchmarks Business metrics Computational Intelligence Constraints Control Elitism Engineering Evolution Feasibility Foundations Genetic algorithms Handling Mathematical Logic and Foundations Mechatronics Methods Multiple objective analysis Optimization Pareto optimization Particle swarm optimization Robotics |
title | Duality evolution: an efficient approach to constraint handling in multi-objective particle swarm optimization |
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