计算机工程与应用2017，Vol.53Issue(9)：41-46,126,7.DOI:10.3778/j.issn.1002-8331.1511-0171
基于演化搜索信息的量子行为粒子群优化算法
Improved QPSO algorithm based on search history
摘要
Abstract
An improved Non-revisited QPSO algorithm based on search history(NrQPSO)is proposed to help prevent pre-mature convergence and stagnate at local optimal solutions. NrQPSO is an integration of the entire search history scheme and a standard Quantum-behaved Particle Swarm Optimization(QPSO). It guarantees that all updated positions are not re-visited before and the diversity of particles is increased by mutation. The search history scheme partitions the continuous search space into overlap sub-regions by using BSP tree. The partitioned sub-region serves as mutation range such that the corresponding mutation is adaptive and parameterless. Compared with other traditional algorithms, the experiment results on eight standard testing functions show that the proposed algorithm is superior regarding the optimization of multimodal and unimodal functions, with enhancement in both convergence speed and precision. Those demonstrate the effectiveness of the algorithm.关键词
量子行为粒子群优化/演化搜索信息/二维空间分割/非重复访问Key words
quantum-behaved particle swarm optimization/search history/binary space partitioning/non-revisited分类
信息技术与安全科学引用本文复制引用
赵吉,程成..基于演化搜索信息的量子行为粒子群优化算法[J].计算机工程与应用,2017,53(9):41-46,126,7.基金项目
国家自然科学基金(No.61300149) （No.61300149）
江苏省青蓝工程资助项目 （）
无锡环境科学与工程研究中心科研启动项目. （）
