带突变算子的量子粒子群优化

17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05) Pub Date : 2005-11-14 DOI:10.1109/ICTAI.2005.104

Jing Liu, Wenbo Xu, Jun Sun

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引用次数: 107

摘要

在量子粒子群优化算法中引入突变机制，提高了量子粒子群优化算法的全局搜索能力，避免了局部极小值的影响。根据QPSO算法的特点，对gbest和mbest的变量分别进行柯西分布变异。在测试函数上的实验结果表明，具有gbest和mbest突变的QPSO的性能都优于未发生突变的QPSO和PSO

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Quantum-behaved particle swarm optimization with mutation operator

The mutation mechanism is introduced into quantum-behaved particle swarm optimization to increase its global search ability and escape from local minima. Based on the characteristic of QPSO algorithm, the variable of gbest and mbest is mutated with Cauchy distribution respectively. The experimental results on test functions show that QPSO with gbest and mbest mutation both performs better than PSO and QPSO without mutation

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17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)

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