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基于目标分解的高维多目标并行进化优化方法
引用本文:巩敦卫, 刘益萍, 孙晓燕, 韩玉艳. 基于目标分解的高维多目标并行进化优化方法. 自动化学报, 2015, 41(8): 1438-1451. doi: 10.16383/j.aas.2015.c140832
作者姓名:巩敦卫  刘益萍  孙晓燕  韩玉艳
作者单位:1.中国矿业大学信息与电气工程学院 徐州 221116;;;2.兰州理工大学电气工程与信息工程学院 兰州 730050
基金项目:国家重点基础研究发展计划(973计划) (2014CB046306-2), 国家自然科学基金(61375067), 江苏省自然科学基金 (BK2012566)资助
摘    要:高维多目标优化问题普遍存在且难以解决, 到目前为止, 尚缺乏有效解决该问题的进化优化方法. 本文提出一种基于目标分解的高维多目标并行进化优化方法, 首先, 将高维多目标优化问题分解为若干子优化问题, 每一子优化问题除了包含原优化问题的少数目标函数之外, 还具有由其他目标函数聚合成的一个目标函数, 以降低问题求解的难度; 其次, 采用多种群并行进化算法, 求解分解后的每一子优化问题, 并在求解过程中, 充分利用其他子种群的信息, 以提高Pareto非被占优解的选择压力; 最后, 基于各子种群的非被占优解形成外部保存集, 从而得到高维多目标优化问题的Pareto 最优解集. 性能分析表明, 本文提出的方法具有较小的计算复杂度. 将所提方法应用于多个基准优化问题, 并与NSGA-II、PPD-MOEA、ε-MOEA、HypE和MSOPS等方法比较, 实验结果表明, 所提方法能够产生收敛性、分布性, 以及延展性优越的Pareto最优解集.

关 键 词:进化优化   高维多目标优化   分解   并行   Pareto占优
收稿时间:2014-12-01
修稿时间:2015-04-08

Parallel Many-objective Evolutionary Optimization Using Objectives Decomposition
GONG Dun-Wei, LIU Yi-Ping, SUN Xiao-Yan, HAN Yu-Yan. Parallel Many-objective Evolutionary Optimization Using Objectives Decomposition. ACTA AUTOMATICA SINICA, 2015, 41(8): 1438-1451. doi: 10.16383/j.aas.2015.c140832
Authors:GONG Dun-Wei  LIU Yi-Ping  SUN Xiao-Yan  HAN Yu-Yan
Affiliation:1. School of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou 221116;;;2. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050
Abstract:Many-objective optimization problem is common in real-world applications, however, so far few evolutionary algorithms are suitable for them due to the difficulties of the problem. A parallel many-objective evolutionary optimization algorithm based on objectives decomposition is proposed. First, the many-objective optimization problem is decomposed into several sub-problems, which contain only some objectives of the original optimization problem together with a constructed objective by aggregating all the other objectives. Then, a multi-population parallel evolutionary algorithm is adopted to solve these sub-problems. The pressure on selecting non-dominated solutions for a sub-problem is improved by taking full advantage of the information obtained from other sub-populations. The final Pareto set of the optimized many-objective is achieved by archiving those sets of non-dominated solutions coming from the sub-populations. The performance of the proposed algorithm on reducing computation complexity is qualitatively analyzed. Furthermore, the algorithm is applied to several benchmark problems and compared with NSGA-II, PPD-MOEA, ε-MOEA, HypE, and MSOPS. The results experimentally demonstrate that the algorithm is strengthened in obtaining solutions with better convergence, distribution and approximation.
Keywords:Evolutionary algorithm  many-objective optimization  decomposition  parallel  Pareto domination
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