自适应二次变异差分进化算法

Differential Evolution Algorithm with Adaptive Second Mutation

提出一种基于群体适应度方差自适应二次变异的差分进化算法.该算法在运行过程中根据群体适应度方差的大小,增加一种新的变异算子对最优个体和部分其他个体同时进行变异操作,以提高种群多样性,增强差分进化算法跳出局部最优解的能力.对几种典型B enchm arks函数进行了测试,实验结果表明,该方法能有效避免早熟收敛,显著提高算法的全局搜索能力.

A new adaptive second mutation differential evolution algorithm （ASMDE） based on the variance of the population＇s fitness is presented. In order to improve the population＇s diversity and the ability of breaking away from the local optimum, according to the value of the variance of the population＇s fitness during the running time, a new mutation operator is adapted to mutate both the best individual and partial other individuals. Several classic Benchmarks functions are tested and the results show that the proposed algorithm can avoid the premature convergence and improves the global convergence ability remarkably.