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《Computer Engineering and Design》 2009-03
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Faster particle swarm optimization with random inertia weight

HUANG Xuan1, ZHANG Jun1,2+, ZHAN Zhi-hui2 (1. School of Software, Sun Yat-Sen University, Guangzhou 510275, China; 2. Departments of Computer Science, Sun Yat-Sen University, Guangzhou 510275, China)  
After the investigation of inertia weight based on six benchmark functions, the impact of inertia weight on the performance of PSO is analyzed. What is more, a new method in which the inertia weight is generated as a random number uniformly distributed in [0.4, 0.6] in order to balance the global search and local search ability of the algorithm is proposed. The experimental results show that the new method is efficient and effective with not only faster convergence rate but also higher quality solutions when compared with linearly decreasing weight (LDW) method.
【Fund】: 国家自然科学基金项目(60573066);; 广东省自然科学基金项目(5003346);; 教育部留学回国人员科研启动基金项目(教外司留[2006]331号)
【CateGory Index】: TP18
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