A new multi-swarm multi-objective particle swarm optimization based on Pareto front set

Yanxia Sun; Barend Jacobus Van Wyk; Zenghui Wang

doi:10.1007/978-3-642-25944-9_27

A new multi-swarm multi-objective particle swarm optimization based on Pareto front set

Yanxia Sun, Barend Jacobus Van Wyk, Zenghui Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.

Original language	English
Title of host publication	Advanced Intelligent Computing Theories and Applications
Subtitle of host publication	With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers
Pages	203-210
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-25944-9_27
Publication status	Published - 2011
Externally published	Yes
Event	7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China Duration: 11 Aug 2011 → 14 Aug 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6839 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Intelligent Computing, ICIC 2011
Country/Territory	China
City	Zhengzhou
Period	11/08/11 → 14/08/11

Keywords

Multi-objective Optimization
Multiple swarms
Pareto front
Particle Swarm Optimization

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-25944-9_27

Cite this

Sun, Y., Van Wyk, B. J., & Wang, Z. (2011). A new multi-swarm multi-objective particle swarm optimization based on Pareto front set. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers (pp. 203-210). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6839 LNAI). https://doi.org/10.1007/978-3-642-25944-9_27

Sun, Yanxia ; Van Wyk, Barend Jacobus ; Wang, Zenghui. / A new multi-swarm multi-objective particle swarm optimization based on Pareto front set. Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers. 2011. pp. 203-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A new multi-swarm multi-objective particle swarm optimization based on Pareto front set",

abstract = "In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.",

keywords = "Multi-objective Optimization, Multiple swarms, Pareto front, Particle Swarm Optimization",

author = "Yanxia Sun and {Van Wyk}, {Barend Jacobus} and Zenghui Wang",

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language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Sun, Y, Van Wyk, BJ & Wang, Z 2011, A new multi-swarm multi-objective particle swarm optimization based on Pareto front set. in Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6839 LNAI, pp. 203-210, 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, 11/08/11. https://doi.org/10.1007/978-3-642-25944-9_27

A new multi-swarm multi-objective particle swarm optimization based on Pareto front set. / Sun, Yanxia; Van Wyk, Barend Jacobus; Wang, Zenghui.
Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers. 2011. p. 203-210 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6839 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.

AB - In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.

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KW - Particle Swarm Optimization

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Sun Y, Van Wyk BJ, Wang Z. A new multi-swarm multi-objective particle swarm optimization based on Pareto front set. In Advanced Intelligent Computing Theories and Applications: With Aspects of Artificial Intelligence - 7th International Conference, ICIC 2011 - Revised Selected Papers. 2011. p. 203-210. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-25944-9_27

A new multi-swarm multi-objective particle swarm optimization based on Pareto front set

Abstract

Publication series

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Keywords

ASJC Scopus subject areas

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Cite this