An Improved QPSO Algorithm Based on Entire Search History
An improved QPSO algorithm based on entire search history(ESH-QPSO)is proposed.ESH-QPSO 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,which helps prevent premature convergence.The entire search history scheme partitions the continuous search space into sub-regions by using BSP tree.The partitioned sub-region servers as mutation range such that the corresponding mutation is adaptive and parameter-less.When sub-regions are formulated as which certain overlap exists between adjacent sub-regions,this allows particle move from a sub-region to another with better fitness.Compared with other traditional algorithms,the experiment results on 8 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.
quantum-behaved particle swarm optimization entire search history adaptive mutate binary space partitioning
Ji Zhao Yi Fu Juan Mei
Research Centre of Environment Science & Engineering;School of IoT Engineering Jiangnan University W School of IoT Engineering Jiangnan University Wuxi,China Research Centre of Environment Science & Engineering Wuxi,China
国际会议
贵阳
英文
74-77
2015-08-18（万方平台首次上网日期，不代表论文的发表时间）