一种基于PSO思想的改进量子遗传算法

An improved quantum genetic algorithm based on theory of PSO

文章提出一种基于PSO思想的改进量子遗传算法。将PSO中的合作机制和记忆功能引入到QGA中,构造种群个体与当前最优解的距离参量,根据每个个体与当前最优解距离大小智能地控制旋转角的大小,使旋转角能够根据个体的进化差异选择不同旋转角的自适应调整进化过程,从而使算法始终保持合适的搜索网格,加快算法收敛,同时也可以保证能够收敛到全局最优,避免早熟;并通过典型函数的测试验证了该算法的可行性和有效性。

This paper proposes an improved quantum genetic algorithm（QGA） based on the theory of particle swarm optimization（PSO）.The cooperation mechanisms and memory function in PSO are introduced into QGA and the distance parameter is constructed between the population individual and the current optimal solution.Quantum rotation counter controlled by the distance parameter can be chosen according to individual differences of evolution to adjust the whole evolution adaptively,so that the algorithm can always keep suitable searching grid to speed up the convergence rate.The algorithm also avoids falling into local minimum and the global optimal solution can be obtained.The test results based on typical functions show that the proposed algorithm is feasible and effective.