A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems

Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best part...

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Bibliographic Details
Published inComplexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 18
Main Authors Ji, Junkai, Liu, Fang, Wu, Shuhuan, Chen, Fei, Lin, Qiuzhen
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
Hindawi Limited
Wiley
Subjects
Algorithms
Angular velocity
Archives & records
Business metrics
Convergence
Decomposition
Evolutionary algorithms
Multiple objective analysis
Novels
Objectives
Optimization
Performance evaluation
Velocity
China
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Summary:Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or decomposition-based method to select the local or best particles, which is not so effective in high-dimensional objective space. To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, θ-DEA, MOEA\D-DD, and SPEA2-SDE), when solving most of the test problems used.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/6238206