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Proceedings Paper

Hybridizing particle swarm optimization with differential evolution based on feasibility rules
Author(s): Junli Zhang; Yongquan Zhou; Hui Deng
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Paper Abstract

This paper presents a novel algorithm named HPSODE for constrained optimization problems. The proposed algorithm integrates particle swarm optimization (PSO) with differential evolution (DE) on the basis of an optimal information sharing mechanism firstly, which avoids premature convergence defects of the single algorithm. Then under the guidance of the feasibility rules, the algorithm quickly finds better feasible solution. Finally, HPSODE is tested on two engineering design problems. Comparisons show that HPSODE has higher computational precision, better robustness and is more effective for solving constrained optimization problem.

Paper Details

Date Published: 14 March 2013
PDF: 8 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876807 (14 March 2013); doi: 10.1117/12.2010544
Show Author Affiliations
Junli Zhang, Guangxi Univ. for Nationalities (China)
Yongquan Zhou, Guangxi Univ. for Nationalities (China)
Hui Deng, Guangxi Univ. for Nationalities (China)


Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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