A Particle Swarm Optimizer with Multi-Stage Linearly-Decreasing Inertia Weight

摘要：

The inertia weight is often used to control the global exploration and local exploitation abilities of particle swarm optimizers (PSO). In this paper, a group of strategies with multi-stage linearly-decreasing inertia weight (MLDW) is proposed in order to get better balance between the global and local search. Six most commonly used benchmarks are used to evaluate the MLDW strategies on the performance of PSOs. The results suggest that the PSO with W5 strategy is a good choice for solving unimodal problems due to its fast convergence speed, and the CLPSO with W5 strategy is more suitable for solving multimodal problems. Also, W5-CLPSO can be used as a robust algorithm because it is not sensitive to the complexity of problems for solving.

作者: Jianbin Xin Guimin Chen Yubao Hai

作者单位: School of Mechatronics, Xidian University, Xian 710071, China School of Electrical Engineering, Xi School of Mechatronics, Xidian University, Xian 710071, China

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 505-508

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）