Setting of Candidate Solutions Considering Confidence Intervals in Differential Evolution

Abstract

Differential evolution is easy to implement, is a good performance optimization algorithm, and is applied in various ways. Candidate solutions in differential evolution are often initialized randomly, but search performance depends greatly on the initial Candidate solutions. It is possible to solve by introducing random elements such as mutation and noise in the evolutionary algorithm, but if introduced beyond necessity, the search speed will be lowered. In the proposed method in this study, we assume that an ideal search point group exists in the confidence interval, and randomly change the next search point candidate within the confidence interval. We confirmed that this proposed method improves the performance of the differential evolution in numerical experiments.