Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled
Quantum-Behaved Particle Swarm Optimization Based on Diversity-Controlled
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DOI：
10.1007/978-3-662-45526-5_13
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发表时间：
2014-11
期刊：
影响因子：
12.8
通讯作者：
Haixia Long;Haiyan Fu;Chunxue Shi
中科院分区：
文献类型：
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作者：
Haixia Long;Haiyan Fu;Chunxue Shi
量子粒子群优化算法（QPSO）是一种全局收敛保证算法，其搜索能力优于原粒子群优化算法，但需要控制的参数较少。但由于种群多样性急剧下降，QPSO算法容易陷入局部最优。为此，本文将多样性控制算法引入量子粒子群算法（QPSO- dc），增强粒子群的多样性，进而提高量子粒子群算法的搜索能力。在基准函数上的实验结果表明，QPSO- dc比QPSO和标准PSO具有更强的全局搜索能力。
Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. But QPSO algorithm is to be easily trapped into local optima as a result of the rapid decline in diversity. So this paper describes diversity-controlled into QPSO (QPSO-DC) to enhance the diversity of particle swarm, and then improve the search ability of QPSO. The experiment results on benchmark functions show that QPSO-DC has stronger global search ability than QPSO and standard PSO.