Global optimization using novel randomly adapting particle swarm optimization approach

Nai Jen Li; Wen June Wang; Chen Chien Hsu; Chih Min Lin

doi:10.1109/ICSMC.2011.6083930

Global optimization using novel randomly adapting particle swarm optimization approach

Nai Jen Li^*, Wen June Wang, Chen Chien Hsu, Chih Min Lin

^*Corresponding author for this work

Department of Electrical Engineering

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

4 Citations (Scopus)

Abstract

This paper proposes a novel randomly adapting particle swarm optimization (RAPSO) approach which uses a weighed particle in a swarm to solve multi-dimensional optimization problems. In the proposed method, the strategy of the RAPSO acquires the benefit from a weighed particle to achieve optimal position in explorative and exploitative search. The weighed particle provides a better direction of search and avoids trapping in local solution during the optimization process. The simulation results show the effectiveness of the RAPSO, which outperforms the traditional PSO method, cooperative random learning particle swarm optimization (CRPSO), genetic algorithm (GA) and differential evolution (DE) on the 6 benchmark functions.

Original language	English
Title of host publication	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages	1783-1787
Number of pages	5
DOIs	https://doi.org/10.1109/ICSMC.2011.6083930
Publication status	Published - 2011
Event	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States Duration: 2011 Oct 9 → 2011 Oct 12

Publication series

Name	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)	1062-922X

Other

Other	2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Country/Territory	United States
City	Anchorage, AK
Period	2011/10/09 → 2011/10/12

Keywords

Randomly adapting particle swarm optimization
evolutionary algorithm
optimization
weighed particle

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

Access to Document

10.1109/ICSMC.2011.6083930

Cite this

Li, N. J., Wang, W. J., Hsu, C. C., & Lin, C. M. (2011). Global optimization using novel randomly adapting particle swarm optimization approach. In 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest (pp. 1783-1787). Article 6083930 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). https://doi.org/10.1109/ICSMC.2011.6083930

Global optimization using novel randomly adapting particle swarm optimization approach. / Li, Nai Jen; Wang, Wen June; Hsu, Chen Chien et al.
2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest. 2011. p. 1783-1787 6083930 (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics).

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

Li, NJ, Wang, WJ, Hsu, CC & Lin, CM 2011, Global optimization using novel randomly adapting particle swarm optimization approach. in 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest., 6083930, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 1783-1787, 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011, Anchorage, AK, United States, 2011/10/09. https://doi.org/10.1109/ICSMC.2011.6083930

Li NJ, Wang WJ, Hsu CC, Lin CM. Global optimization using novel randomly adapting particle swarm optimization approach. In 2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest. 2011. p. 1783-1787. 6083930. (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics). doi: 10.1109/ICSMC.2011.6083930

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abstract = "This paper proposes a novel randomly adapting particle swarm optimization (RAPSO) approach which uses a weighed particle in a swarm to solve multi-dimensional optimization problems. In the proposed method, the strategy of the RAPSO acquires the benefit from a weighed particle to achieve optimal position in explorative and exploitative search. The weighed particle provides a better direction of search and avoids trapping in local solution during the optimization process. The simulation results show the effectiveness of the RAPSO, which outperforms the traditional PSO method, cooperative random learning particle swarm optimization (CRPSO), genetic algorithm (GA) and differential evolution (DE) on the 6 benchmark functions.",

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Global optimization using novel randomly adapting particle swarm optimization approach

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Keywords

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