摘要
针对量子粒子群优化算法容易出现早熟收敛到局部最优解等缺点,本文在量子粒子群算法的基础上,提出一种基于差分进化的混沌量子粒子群算法。该算法将Logistic映射的混沌序列引入粒子中,增强了粒子的多样性;其次,对早熟的粒子进行交叉选择操作,使得粒子可以跳出局部最优值。实验结果表明,该算法相比于量子粒子群算法有较快的收敛速度和较好的收敛性能。
In view of the shortcomings of the quantum-behaved particle swarm optimization algorithm that is prone to premature convergence to the local optimal solution, this paper proposed a chaos quantum-behaved particle swarm algorithm based on differ- ential evolution. In order to enhance the diversity of the particles, the chaotic sequences of Logistic maps are introduced into the particle in this algorithm. Then, the crossover and mutation operation is performed for the premature particles which can avoid the local optimum in the latter part of the particle. Experimental resuhs show that the proposed algorithm has faster convergence speed and better convergence performance than quantum particle swarm algorithm.
出处
《计算机与现代化》
2017年第8期22-25,30,共5页
Computer and Modernization
基金
陕西省教育厅专项科研计划资助项目(15JK1381)
关键词
量子粒子群算法
差分算法
混沌
quantum behaved particle swarm optimization
differential evolution
chaos
