电子学报2016，Vol.44Issue(12)：2900-2907,8.DOI:10.3969/j.issn.0372-2112.2016.12.013
基于演化历史信息的自变异协同量子行为粒子群优化算法
An I mproved Cooperative QPSO AIgorithm with Adaptive Mutation Based on Enti re Search History
摘要
Abstract
An improved cooperative QPSO algorithm with adaptive mutation based on entire search history (ESH-CQPSO)is proposed.The proposed algorithm employs a binary space partitioning tree structure to memorize the positions and the fitness values of the evaluated solution.The cooperation mechanism between the solutions can ensure enhanced search capabilities,improve the optimize performance and prevent premature convergence.Benefiting from the space partitio-ning scheme,a fast fitness function approximation using the archive is obtained.The approximation is used to improve the mutation strategy in ESH-CQPSO.The resultant mutation is adaptive and parameter-less.Compared with other traditional al-gorithms,the experiment results on standard testing functions show that the proposed algorithm is superior regarding the opti-mization of multimodal and unimodal functions,with enhancement in both convergence speed and precision,which demon-strate the effectiveness of the algorithm.关键词
量子行为粒子群优化/演化历史信息/自适应变异/二维空间分割/协同方式Key words
quantum-behaved particle swarm optimization(QPSO)/entire search history/adaptive mutate/binary space partitioning/cooperative method分类
信息技术与安全科学引用本文复制引用
赵吉,傅毅,梅娟..基于演化历史信息的自变异协同量子行为粒子群优化算法[J].电子学报,2016,44(12):2900-2907,8.基金项目
国家自然科学基金（No．61300149，No．61105128，No．61502203）；江苏省青蓝工程资助（No．2012-16）；江苏省自然科学基金（No． BK20131106）；江南大学自主科研重点计划（No．JUSRP51410B）；中国博士后科学基金 （）
