A Hybrid Algorithm Based on Improved Crossover PSO and Powell

摘要：

This paper presents an improved crossover PSO by introducing the linear inertia weight and linear adaptive learning rates to crossover PSO to overcome the shortcoming of premature convergence and trapping in local optimum solution. Further more, a hybrid algorithm based on improved crossover PSO and Powell is proposed to enhance both global and local searching ability. The simulation results show the improved performance.

关键词： Particle Swarm Optimization Powell Algorithm Function Optimization

作者: Yuling Bo Weiling Zhu Jingqing Jiang Chuyi Song

作者单位: College of Mathematics Inner Mongolia University for Nationalities Tongliao, China College of Computer Science and Technology Inner Mongolia University for Nationalities Tongliao, Chi

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Automation Engineering(ICCAE 2011)(2011年第三届IEEE计算机与自动化工程国际会议)

会议地点: 重庆

会议语种:英文

页码: 379-382

在线出版日期: 2011-01-21（万方平台首次上网日期，不代表论文的发表时间）