An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Weian Guo, Lei Zhu, Lei Wang, Qidi Wu and Fanrong Kong
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Weian Guo: Key Laboratory of Intelligent Computing & Signal Processing (Ministry of Education), Anhui University, Hefei 230039, China
Lei Zhu: Key Lab of Information Network Security Ministry of Public Security, Shanghai 201112, China
Lei Wang: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Qidi Wu: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Fanrong Kong: School of Software Engineering, Tongji University, Shanghai 201804, China

Mathematics, 2019, vol. 7, issue 5, 1-12

Abstract: Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

Keywords: diversity maintenance; particle swarm optimizer; entropy; large scale optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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