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

Abstract: An improved Non-revisited QPSO algorithm based on search history（NrQPSO）is proposed to help prevent premature 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.

Key words: quantum-behaved particle swarm optimization, search history, binary space partitioning, non-revisited