免疫粒子群算法的改进及应用

Modified immune particle swarm optimization algorithm and its application

在现有的免疫粒子群算法基础上,增加了交叉和高频变异操作,以保证种群进化的多样性,克服粒子群算法的早熟现象。本算法通过柯西变异提高算法的全局搜索能力;通过高斯变异提高算法的局部搜索能力。此外,为解决随机的、没有指导的交叉变异操作可能引起的退化现象,引入了疫苗提取和疫苗接种策略。仿真结果表明算法的收敛速度和精度都有明显提高。

In this paper,a modified Particle Swarm Optimization（PSO） algorithm with immunity was proposed.The intersection operation and high frequency mutation were introduced to keep the population＇s diversity and avoid early ripe of PSO.The algorithm＇s global searching ability was improved through Cauchy mutation,and local searching ability was improved by Gaussian mutation.In addition,the vaccine operation was introduced to solve the potential degradation caused by the probability distributed intersection and mutation.The simulation results show that the evolution speed and convergence precision of proposed algorithm are improved.