具有Pbest引导机制的适应性多策略差分进化算法  

Adaptive Multiple Strategy Differential Evolution Algorithm with Guiding Scheme of Pbest

作　　者：向万里[1,2] 马寿峰[1] 安美清[2] 

机构地区：[1]天津大学系统工程研究所天津300072 [2]兰州交通大学交通运输学院兰州730070

出　　处：《模式识别与人工智能》2013年第8期711-721,共11页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.70971094);天津市科技支撑计划项目(No.08ZCKFSF01000);高等学校博士学科点专项项目(No.20090032110033)资助

摘　　要：为进一步提升差分进化算法的收敛性能,提出一种具有Pbest引导机制的适应性多策略差分进化算法(AMSDE).AMSDE设计交叉概率控制参数库、变异尺度参数库及差分变异策略库,其中,交叉概率采用Logistic混沌序列来模拟,尺度参数采用线性变化机制产生,差分变异策略库采用6个常用的差分变异策略组成,并给出算法运行的框架.最后,基于25个标准测试函数的仿真结果表明,AMSDE的收敛性能优于现存的其它差分进化算法,具有较好的收敛精度及收敛速度.To improve the convergence performance of differential evolution algorithm, an adaptive multiple strategy differential evolution algorithm （AMSDE） with guiding scheme of Pbest is proposed. The library of control parameters of the crossover probability, the library of scale parameters of the mutation and the library of the differential mutation strategy are designed in AMSDE. Thereinto, the crossover probability is generated by Logistic chaotic systems, the scale parameter is produced by means of a linear changing scheme, and the library of differential mutation strategy consists of six widely used differential mutation strategies. Subsequently, the framework of AMSDE is given. Finally, simulation results on 25 benchmark test functions demonstrate that AMSDE achieves better convergence precision and a higher convergence speed. And AMSDE outperforms the two state-of-the-art variants of differential evolution algorithms, JADE and CODE.

关 键 词：差分进化算法 Logistic混沌系统 Pbest引导机制 多差分变异策略 移位旋转函数 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]