Hybridizing particle swarm optimization with differential evolution based on feasibility rules

Junli Zhang; Yongquan Zhou; Hui Deng

doi:10.1117/12.2010544

14 March 2013 Hybridizing particle swarm optimization with differential evolution based on feasibility rules

Junli Zhang, Yongquan Zhou, Hui Deng

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 876807 (2013) https://doi.org/10.1117/12.2010544
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

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.

Citation Download Citation

Junli Zhang, Yongquan Zhou, and Hui Deng "Hybridizing particle swarm optimization with differential evolution based on feasibility rules", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876807 (14 March 2013); https://doi.org/10.1117/12.2010544

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