Super-high Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

摘要：

　　Due to the existence of large numbers of local and global optima of super-high dimension complex functions,general Particle Swarm Optimizer (PSO) methods are slow speed on convergence and easy to be trapped in local optima.In this paper,an Adaptive Particle Swarm Optimizer(APSO) is proposed,which employ an adaptive inertia factor and dynamic changes strategy of search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions,and to quicken convergence speed,avoid premature problem,economize computational expenses,and obtain global optimum.We test the proposed algorithm and compare it with other published methods on several super-high dimension complex functions,the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

关键词： Particle Swarm Optimizer (PSO) Complex functions Convergenc premature problem adaptive

作者: Ying Zhang Boqin Liu Hanrong Chen

作者单位: College of Computer and Information ScienceSouthwest UniversityChongqing.China 400715

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1830-1835

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）