A Novel Memetic Algorithm for Unconstrained Optimization

摘要：

　　This paper describes a novel Memetic Algorithm for unconstrained optimization. The proposed approach aims to add a probabilistic procedure to determine if employing pattern search during a specific Particle Swarm Optimization generation. To verify the effectiveness of the proposed approach, several continuous functions are selected to test the proposed approach in comparison to conventional pattern search and the conventional PSO. Moreover, two kinds of integration schema for pattern search and PSO are also compared with the proposed approach. Experimental results demonstrate that the proposed approach is extremely effective and efficient at locating global optimal solutions for unconstrained optimization.

关键词： Function Optimization Particle Swarm Optimization Pattern Search hybrid optimization

作者: Yukun Bao Zhongyi Hu Tao Xiong Yunfei Yang

作者单位: School of Management,Huazhong University of Science and Technology, Wuhan, 430074, China

会议类型: 国际会议

会议名称: 第8届国际最优化方法及应用大会

会议地点: 上海

会议语种:英文

页码: 342-343

在线出版日期: 2010-12-10（万方平台首次上网日期，不代表论文的发表时间）