一种改进的多目标混合差分进化算法  

Improved multi-objective hybrid differential evolution algorithm

作　　者：王筱珍[1] 俞国燕[2] 

机构地区：[1]广东海洋大学信息学院,广东湛江524088 [2]广东海洋大学工程学院,广东湛江524088

出　　处：《计算机应用研究》2014年第5期1332-1335,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(50675069);广东省科技计划资助项目(2010B020314005);广东省教育部产学研结合资助项目(2012B010300023)

摘　　要：将差分进化算法(DE)用于多目标优化问题,提出了一种精英保留和进化进程中非支配解集迁移操作的差分进化算法,以保证所求得多目标优化问题Pareto最优解的多样性。采用双群体约束处理技术,构建进化群体的Pareto非支配解外部存档集,并进行基于非支配解集的迁移操作,以增加非支配解的数目和质量。用多个经典测试函数测试的结果表明,与标准DE相比,该方法收敛到问题的Pareto前沿效果良好,能有效保持Pareto最优解多样性与收敛之间的平衡。By using the differential evolution algorithm （DE） to solve multi-objective optimization problems, this paper pro- posed a Pareto optimal solution migration based differential evolution for multi-objective optimization （PSDEMO） to guarantee the diversity of Pareto optimal solution. It adopted the elitist strategy in the algorithm, and archived Pareto non-dominance solu- tions found in the evolution operation dynamically with the evolution process. In addition, it used all the non-dominance solu- tions in the archive to do migration operation after mutation and crossover operation of DE to increase the number and quality of non-dominated solutions, Compared with standard DE, simulation results show that the PSDEMO not only helps to improve the quantity of the Pareto non-dominance solution, but also has good balance keeping ability between the diversity and conver- gence.

关 键 词：差分进化算法 多目标优化 迁移操作 精英保留 非支配解 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]