《计算机应用研究》|Application Research of Computers

基于分解和多策略变异的多目标差分进化算法

Multi-objective differential evolution algorithm based on decomposition and multi-strategy mutation

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作者 童旅杨,董明刚,敬超
机构 桂林理工大学 a.信息科学与工程学院;b.广西嵌入式技术与智能系统重点实验室,广西 桂林 541004
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文章编号 1001-3695(2019)07-008-1955-05
DOI 10.19734/j.issn.1001-3695.2018.01.0028
摘要 差分进化是一种有效的优化技术,已成功应用于多目标优化问题,但也存在Pareto最优集合的收敛慢和多样性差等问题。针对上述不足,提出了一种基于分解和多策略变异的多目标差分进化算法(MODE/DMSM)。该算法利用基于分解的方法将多目标优化问题分解为多个单目标优化问题;通过高效的非支配排序方法选择具有良好收敛性和多样性的解来指导差分进化过程;采用了多策略变异方法来平衡进化过程中的收敛性和多样性。在ZDT和DTLZ的10个测试函数上的仿真结果表明,所提算法在Parato最优集合的收敛性和多样性方面优于其他六种代表性多目标优化算法。
关键词 多目标优化; 差分进化; 分解; 多策略变异
基金项目 国家自然科学基金资助项目(61563012,61203109)
广西自然科学基金资助项目(2014GXNSFAA118371,2015GXNSFBA139260)
广西嵌入式技术与智能系统重点实验室基金资助项目
本文URL http://www.arocmag.com/article/01-2019-07-008.html
英文标题 Multi-objective differential evolution algorithm based on decomposition and multi-strategy mutation
作者英文名 Tong Lyuyang, Dong Minggang, Jing Chao
机构英文名 a.College of Information Science & Engineering,b.Guangxi Key Laboratory of Embedded Technology & Intelligent System,Guilin University of Technology,Guilin Guangxi 541004,China
英文摘要 Differential evolution algorithm is an efficient optimization technique that has been successfully applied to multiobjective optimization problems. However, there are also some defects, i. e. the slow convergence and poor diversity of the Pareto optimal set. Addressing these issues, this paper presented a multi-objective differential evolution algorithm based on decomposition and multi-strategy mutation(MODE/DMSM). MODE/DMSM utilized the decomposition-based approach to decompose a multi-objective optimization problem into multiple single-objective optimization problems. Moreover, MODE/DMSM adopted the efficient non-dominated sorting approach to select solutions which had both good convergence and diversity to guide the differential evolutionary process. Eventually, MODE/DMSM employed the multi-strategy mutation approach to balance the convergence and diversity in the evolutionary process. The results of simulations on 10 test functions of ZDT and DTLZ show that MODE/DMSM outperforms than the other six representative multi-objective optimization algorithms in terms of the good convergence and diversity of the Pareto optimal set.
英文关键词 multi-objective optimization; differential evolution(DE); decomposition; multi-strategy mutation
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收稿日期 2018/1/17
修回日期 2018/3/9
页码 1955-1959,1990
中图分类号 TP18;TP301.6
文献标志码 A