An Extended Mutation Concept for the Local Selection Based Differential Evolution Algorithm

A new mutation concept is proposed to generalize local selection based Differential Evolution algorithm to work in general multi-modal problems. Three variations of the proposed method are compared with classic Differential Evolution algorithm using a set of five well known test functions and their variants. The general idea of the new mutation operation is to divide the mutation into two parts: the local and global mutation. The global mutation works as a migration operator allowing the algorithm perform global search efficiently, while the local mutation improves the efficiency of local search. The results show that the concept of global mutation is able to generalize the good performance of local selection based Differential Evolution from convex uni-modal functions to general non-convex and multi-modal problems. Among the tested functions, the new method was able to outperform the classic Differential Evolution ill all but one. A limited analysis of the effects of control parameters to the performance of the algorithm is also done.