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
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Zusammenfassung: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.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2754271