Global optimization using novel randomly adapting particle swarm optimization approach

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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 languageEnglish
Title of host publication2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages1783-1787
Number of pages5
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States
Duration: 9 Oct 201112 Oct 2011

Publication series

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

Conference

Conference2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
Country/TerritoryUnited States
CityAnchorage, AK
Period9/10/1112/10/11

Keywords

  • Randomly adapting particle swarm optimization
  • evolutionary algorithm
  • optimization
  • weighed particle

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