A Modified Glowworm Swarm Optimization for Multimodal Functions

摘要：

Glowworm swarm optimization (GSO) is a novel algorithm for the simultaneous computation of multiple optima of multimodal functions, which is a swarm intelligence based optimization algorithm, such as ant colony optimization (ACO) and particle swarm optimization (PSO). In the optimization of multimode functions, GSO performs very well in terms of the number of peaks captured. In this paper, we propose a modified glowworm swarm optimization algorithm. Variable step-size movement strategy and the self-exploration behavior of glowworms have been studied according to the phenomena of nature. In this way, the behavior of glowworms accords with the biological natural law even more, and easily find multiple optima of a given multimodal function. Simulation experiments on three standard multimodal functions are carried out, and the results show that this modified optimization strategy has nice convergence ability and precision. And the convergence speed of the algorithm is greatly improved.

关键词： Multimodal functions optimization Ant colony optimization Particle swarm optimization Glowworm swarm optimization Variable step length movement Self-exploration behavior

作者: Yu-Li Zhang Xiao-Ping Ma Ying Gu Yan-Zi Miao

作者单位: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, School of Science, Dalian Jiaotong University, Dalian, Liaoning 116028 PRC

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 2070-2075

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）