通信学报2017，Vol.38Issue(8)：66-78,13.DOI:10.11959/j.issn.1000-436x.2017165
无惯性自适应精英变异反向粒子群优化算法
Non-inertial opposition-based particle swarm optimization with adaptive elite mutation
摘要
Abstract
Non-inertial opposition-based particle swarm optimization with adaptive elite mutation (NOPSO) was proposed to overcome the drawbacks,such as,slow convergence speed,falling into local optimization,of opposition-based particle swarm optimization.In addition to increasing the diversity of population,two mechanisms were introduced to balance the contradiction between exploration and exploitation during its iterations process.The first one was non-inertial velocity (NIV) equation,which aimed to accelerate the process of convergence of the algorithm via better access to and use of environmental information.The second one was adaptive elite mutation strategy (AEM),which aimed to avoid trap into local optimum.Experimental results show NOPSO algorithm has stronger competitive ability compared with opposition-based particle swarm optimizations and its varieties in both calculation accuracy and computation cost.关键词
无惯性速度更新式/一般性反向学习/自适应精英变异/粒子群优化Key words
non-inertial velocity equation/generalized opposition-based learning/adaptive elite mutation/particle swarm optimization分类
信息技术与安全科学引用本文复制引用
康岚兰,董文永,宋婉娟,李康顺..无惯性自适应精英变异反向粒子群优化算法[J].通信学报,2017,38(8):66-78,13.基金项目
国家自然科学基金资助项目(No.61170305,No.60873114) （No.61170305,No.60873114）
江西省教育厅科学技术研究基金资助项目(No.GJJ161568,No.GJJ151521)The National Natural Science Foundation of China (No.61170305,No.60873114),Science and Technology Research in Department of Education of Jiangxi Province (No.GJJ161568,No.GJJ151521) （No.GJJ161568,No.GJJ151521）
