Opposition-Based Learning Fully Informed Particle Swarm Optimizer without Velocity

摘要：

　　By applying full information and employing the notion of opposition based learning, a new opposition based learning fully information particle swarm optimiser without velocity is proposed for optimization problems.Different from the standard PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning in the algorithm.Besides, all personal best positions are considered to update particle position.The theoretical analysis for the proposed algorithm implies that the particle of the swarm tends to converge to a weighted average of all personal best position.Because of discarding the particle velocity, and using full information and opposition-based learning, the algorithm is the simpler and more effective.The proposed algorithm is applied to some well known benchmarks.The relative experimental results show that the algorithm achieves better solutions and faster convergence.

关键词： particle swarm optimizer opposition-based learning full information

作者: Ying Gao Lingxi Peng Fufang Li Miao Liu Waixi Liu

作者单位: Department of Computer Science and Technology, Guangzhou University,No.230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center,Guangzhou, 510006, P.R.China

会议类型: 国际会议

会议名称: 4th international Conference,ICSI2013(第4届群体智能国际会议)

会议地点: 哈尔滨

会议语种:英文

页码: 79-86

在线出版日期: 2013-06-12（万方平台首次上网日期，不代表论文的发表时间）