Distributed learning particle swarm optimizer for global optimization of multimodal problems

doi:10.1007/s11704-016-5373-1

Front. Comput. Sci.

2018, Vol. 12

Issue (1) : 122-134 https://doi.org/10.1007/s11704-016-5373-1

RESEARCH ARTICLE

Distributed learning particle swarm optimizer for global optimization of multimodal problems

Geng ZHANG¹, Yangmin LI²(

), Yuhui SHI³

¹. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macao, China
². Industrial and Systems Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong, China
³. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China

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Abstract

Particle swarm optimizer (PSO) is an effective tool for solving many optimization problems. However, it may easily get trapped into local optimumwhen solving complex multimodal nonseparable problems. This paper presents a novel algorithm called distributed learning particle swarm optimizer (DLPSO) to solve multimodal nonseparable problems. The strategy for DLPSO is to extract good vector information from local vectors which are distributed around the search space and then to form a new vector which can jump out of local optima and will be optimized further. Experimental studies on a set of test functions show that DLPSO exhibits better performance in solving optimization problems with few interactions between variables than several other peer algorithms.

Keywords particle swarm optimizer (PSO) orthogonal experimental design (OED) swarm intelligence

Corresponding Author(s): Yangmin LI

Just Accepted Date: 30 September 2016 Online First Date: 30 October 2017 Issue Date: 12 January 2018

Cite this article:

Geng ZHANG,Yangmin LI,Yuhui SHI. Distributed learning particle swarm optimizer for global optimization of multimodal problems[J]. Front. Comput. Sci., 2018, 12(1): 122-134.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5373-1
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I1/122

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