适用于高维优化问题的改进进化策略

Improved evolution strategies for high-dimensional optimization

针对高维连续函数优化问题,研究了CES(classical evo lution strateg ies)的变异方式、繁殖方式,提出了全基因变异与单基因变异的概念,通过理论分析和仿真计算论证了单基因变异比全基因变异具有更好的局部搜索能力和少的计算开销;针对CES策略参数(变异幅度)随机性过强,不能很好地跟踪进化过程的问题,提出了随着进化过程递减的策略参数.最后,建立了单基因Gauss变异与均匀变异相结合、使用精英繁殖、递减型策略参数、小种群规模的(μ+λ+k)-ES,给出了一组100维典型测试函数的仿真计算结果.

For high-dimensional continuous function optimization, manners of mutation and reproduction of classical evolution strategies （CES） are investigated. Concepts of all-gene mutation and single-gene mutation are proposed and it is proofed through theoretical analysis and simulation that single-gene mutation significantly outperforms all-gene mutation in both local searching capability and computation costs. Since parameters of CES cannot properly track the process of evolution because of their strong randomness, a strategy parameter is introduced which descends in the process of evolution. Finally, a new ES, called （μ＋λ＋k） -ES, is established which is characterized with single-gene Gaussian plus uniform mutation, elitist-reproduction, descending strategy parameter and small population size. Simulation results on a set of 100-dimensional typical test functions are presented.