An Improved Genetic Algorithm with Initial Population Diversity Based on Orthogonal Experiment Design

摘要

This paper presents a method of generating the initial population of genetic algorithms (GAs) for continuous global optimization by using Orthogonal Experiment Design instead of a pseudo-randoM sequence. The generated initial population is much more evenly distributed. which can avoid causing rapid clustering around an arbitrary local optimal. We design a GA based on this initial population for global numerical optimization with continuous variables. So. the obtained population is more evenly distributed and the GA process is more robust. We executed the proposed algorithm to solve a multimodal functioa The results showed that the proposed algorithm can find globally optimal solutions.