An Improved Cooperative QPSO Algorithm with Adaptive Mutation Based on Entire Search History
ZHAO Ji1,2, FU Yi1, MEI Juan1
1. Research Centre of Environment Science & Engineering, Wuxi, Jiangsu 214063, China;
2. School of IoT Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
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 partitioning 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 algorithms,the experiment results on 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,which demonstrate the effectiveness of the algorithm.
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.
ZHAO Ji, FU Yi, MEI Juan. An Improved Cooperative QPSO Algorithm with Adaptive Mutation Based on Entire Search History. Acta Electronica Sinica, 2016, 44(12): 2900-2907.
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2016, Vol. 44 
