A piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimization

This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new single-objective optimization approach which requires no adjustment of its param...

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Veröffentlicht in:Information sciences Jg. 218; S. 85 - 102
Hauptverfasser: Xu, Wenxing, Geng, Zhiqiang, Zhu, Qunxiong, Gu, Xiangbai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.01.2013
Schlagworte:
Chaotic optimization
Constrained optimization
Particle swarm optimization
Piecewise linear chaotic map
Sequential quadratic programming
Sequential quadratic programming
Chaotic optimization
Piecewise linear chaotic map
Particle swarm optimization
Constrained optimization
ISSN:0020-0255, 1872-6291
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Zusammenfassung:This paper presents a novel robust hybrid particle swarm optimization (RHPSO) based on piecewise linear chaotic map (PWLCM) and sequential quadratic programming (SQP). The aim of the present research is to develop a new single-objective optimization approach which requires no adjustment of its parameters for both unconstrained and constrained optimization problems. This novel algorithm makes the best of ergodicity of PWLCM to help PSO with the global search while employing the SQP to accelerate the local search. Five unconstrained benchmarks, eighteen constrained benchmarks and three engineering optimization problems from the literature are solved by using the proposed hybrid approach. The simulation results compared with other state-of-art methods demonstrate the effectiveness and robustness of the proposed RHPSO for both unconstrained and constrained problems of different dimensions.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2012.06.003