A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems

This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value. The proposed method features an index-based ring topology to induce stable niches th...

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Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 22; H. 5; S. 805 - 817
Hauptverfasser:	Yue, Caitong, Qu, Boyang, Liang, Jing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Benchmark functions Convergence Mathematical programming Measurement multimodal multiobjective Multiple objective analysis Object recognition Optimization Pareto optimization Pareto optimum Particle swarm optimization particle swarm optimization (PSO) ring topology Rings (mathematics) Topology Topology optimization
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
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Abstract	This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value. The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces. In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms. An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness.
AbstractList	This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value. The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces. In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms. An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness.
Author	Liang, Jing Qu, Boyang Yue, Caitong
Author_xml	– sequence: 1 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong email: zzuyuecaitong@163.com organization: Industrial Technology Research Institute and School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Boyang orcidid: 0000-0001-7539-3927 surname: Qu fullname: Qu, Boyang email: qby1984@hotmail.com organization: School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 3 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing email: liangjing@zzu.edu.cn organization: Industrial Technology Research Institute and School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
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SubjectTerms	Algorithm design and analysis Algorithms Benchmark functions Convergence Mathematical programming Measurement multimodal multiobjective Multiple objective analysis Object recognition Optimization Pareto optimization Pareto optimum Particle swarm optimization particle swarm optimization (PSO) ring topology Rings (mathematics) Topology Topology optimization
Title	A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems
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