双心扰动量子粒子群优化算法研究  

Research on QPSO Algorithm of Double Core Disturbance

作　　者：王安龙[1] 何建华[1] 陈松[1] 刘怀远[1] 

机构地区：[1]西北工业大学电子信息学院,西安710000

出　　处：《计算机工程》2014年第7期193-196,201,共5页Computer Engineering

摘　　要：针对量子粒子群优化算法早熟收敛的问题,提出一种双心扰动的变异机制。对粒子的势能中心和粒子群的重心进行自适应柯西变异,发挥两者在进化后期的协同引导能力,以提高进化后期粒子群对新空间的开拓能力。对4个典型测试函数进行仿真实验,结果表明,对于单峰函数优化,双心扰动变异机制的优化效果比只采用势能中心、重心和全局最好位置变异的优化效果提高36.42%以上;对于多峰函数优化,其优化效果提高32.84%以上。Aiming at the problem of the premature convergence of Quantum Particle Swarm Optimization（QPSO） algorithm. This paper introduces a double core disturbance mutation mechanism. It uses adaptive Cauchy mutation to mutate the potential energy of the particle center and the center of gravity of the particle swarm and make full use of the guiding ability of the two centers in the late part of evolution. It adopts four typical functions to conduct simulation experiment, results show that double core disturbances mutation mechanism optimization is better than the strategy of only potential energy center, the center of gravity or global optimal mutation at least 36.42%, and the optimization results improve at least 32.84% for multimodal function optimization.

关 键 词：量子粒子群优化算法 势能中心 全局最好位置 柯西变异 函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]