Hybrid Particle Swarm Optimization with BFGS Method

摘要：

To overcome the problem of premature convergence on particle swarm optimization (PSO) in optimizing multimodal function, this paper proposed a hybrid algorithm combining PSO and BFGS method (PSO-BFGS) and used a special mutation to make particles escape local minima.Three benchmark functions were selected as the test functions. The result shows that the hybrid PSO-BFGS algorithm can not only effectively locate the global optimum,but also have a rather high convergence speed. This hybrid algorithm is a promising approach for solving global optimization problems.

关键词： Particle Swarm Optimization Hybrid Algorithm Global Optimum BFGS Method

作者: Kezhong Lu Xiaoying Shuai

作者单位: Department of Computer Science, Chizhou Teachers College, Chizhou 247000, China

会议类型: 国际会议

会议名称: 2006 International Symposium on Distributed Computing and Applications to Business,Engineering and Science(2006年国际电子、工程及科学领域的分布式计算应用学术研讨会)

会议地点: 杭州

会议语种:英文

页码: 433-435

在线出版日期: 2006-10-12（万方平台首次上网日期，不代表论文的发表时间）