An Improved Genetic Algorithm with Initial Population Diversity Based on Orthogonal Ezperiment Design
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.
Orthogonal Ezperiment Design initial population diversity genetic algorithm global optimization
T. Xu B. Qiu W. J. Zuo R. C. Li
Department of Mechanics, Jilin University, Nanling Campus, Changchun 130025, P.R. China
国际会议
The First World Congress on Global Optimization in Engineering & Science(第一届工程与科学全局优化国际会议 WCGO2009)
长沙
英文
809-814
2009-06-01（万方平台首次上网日期，不代表论文的发表时间）