期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：2A
页码：52-52~58
出版社：International Journal of Computer Science and Network Security
摘要：It is difficult for the existing multi-population genetic algorithms with space partition to be successfully applied to multi-objective optimization problems. Multi-population genetic algorithms with space partition for multi-objective optimization problems are designed in this paper in allusion to the characteristics of multi-objective optimization problems. A complicated optimization problem is converted into several simple optimization problems. Crossover operator for an intra-population evolution has a direction by using information from the super individual archive. The frequency of updating the super individual archive decreases via pre-selecting optimal solutions submitted to the super individual archive. The search scope of a population is expanded via an inter-population evolution. It is shown from analysis that the computational complexity of the algorithm in this paper decreases evidently. The efficiency of the algorithm in this paper is validated through a complicated benchmark multi-objective optimization problem.
关键词：Genetic algorithms, Multi-population, Space partition, Multi-objective optimization problems.
