Swarm-Core Evolutionary Particle Swarm Optimization in Dynamic Optimization Environments

Dou Quansheng; Zhou Chunguang; Xu Zhongyu; Pan Guanyu

Journal of Computer Research and Development > 2006 > 43(1): 89-95.

Dou Quansheng, Zhou Chunguang, Xu Zhongyu, Pan Guanyu. Swarm-Core Evolutionary Particle Swarm Optimization in Dynamic Optimization Environments[J]. Journal of Computer Research and Development, 2006, 43(1): 89-95.

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Swarm-Core Evolutionary Particle Swarm Optimization in Dynamic Optimization Environments

1(School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005) 2(College of Computer Science and Technology, Jilin University, Changchun 130012) 3(College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012)

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Published Date: January 14, 2006

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Abstract

Abstract

The particle swarm optimization (PSO) method was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully in various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. The concept of “swarm-core” is defined in this paper, based on this concept an improved PSO is proposed, which is swarm-core evolutionary particle swarm optimization (SCEPSO). In order to enhance the optimization power of the swarm, the particle swarm are divided into three sub-swarms and each sub-swarm has different job in SCEPSO. At same time the effectiveness of SCEPSO in tracking changing extrema are investigated, experiments for the three types of dynamic optimization models indicate that the SCEPSO can track a continuously changing solution reliably and accurately compared with PSO.
- swarm intelligence,
- particle swarm optimization,
- dynamic optimization problem,
- swarm core,
- sub-swarm

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