![](/images/graphics-bg.png)
A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems
Joint Authors
Lin, Qiuzhen
Ji, Junkai
Chen, Fei
Wu, Shuhuan
Liu, Fang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-17
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Fei& Wu, Shuhuan& Liu, Fang& Ji, Junkai& Lin, Qiuzhen. 2020. A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1142823
Modern Language Association (MLA)
Chen, Fei…[et al.]. A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1142823
American Medical Association (AMA)
Chen, Fei& Wu, Shuhuan& Liu, Fang& Ji, Junkai& Lin, Qiuzhen. A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1142823
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142823