基于t分布变异的自适应差分进化算法  

Self-adaptive differential evolution algorithm using mutations based on the t distribution

作　　者：刘兴阳[1] 毛力[1] 

机构地区：[1]江南大学,物联网工程学院,轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《计算机工程与应用》2012年第2期127-129,共3页Computer Engineering and Applications

基　　金：农业部淡水鱼类遗传育种和养殖生物学重点实验室项目(No.BZ2009-07)

摘　　要：为了更好地提高差分进化算法的全局探索和局部开发能力,提出了一种改进的差分进化算法。在该算法中,引入t分布变异算子将高斯变异和柯西变异的优点结合起来,根据以往的进化经验自适应地调整进化策略及交叉概率。通过四个典型的Benchmarks函数的测试结果表明算法具有良好的性能。A novel differential evolution algorithm is proposed to improve the exploration and exploitation capabilities of differential evolution（DE）.In this algorithm,a new mutation operator following the t distribution is used to integrate the advantages of Gaussian and Cauchy mutation,while both the mutation strategy and crossover probability can be gradually self-adapted by learning from their previous successful experience.Experimental studies are carried out on four classical Benchmark functions,and the computational results show that the algorithm has fast convergence,high accuracy more robustness.

关 键 词：差分进化 T分布 进化策略自适应 交叉概率自适应 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]