基于目标分解的高维多目标并行进化优化方法

巩敦卫; 刘益萍; 孙晓燕; 韩玉艳

doi:10.16383/j.aas.2015.c140832

基于目标分解的高维多目标并行进化优化方法

doi: 10.16383/j.aas.2015.c140832

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

详细信息

作者简介:
巩敦卫中国矿业大学信息与电气工程学院教授.1999年在中国矿业大学获博士学位.主要研究方向为进化计算与应用.E-mail:dwgong@vip.163.com

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- 文章访问数: 2021
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- 被引次数: 0
出版历程
- 收稿日期: 2014-12-01
- 修回日期: 2015-04-08
- 刊出日期: 2015-08-20

Parallel Many-objective Evolutionary Optimization Using Objectives Decomposition

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

Funds:

Supported by National Basic Research Program of China (973 Program) (2014CB046306-2), National Natural Science Foundation of China (61375067), and Natural Science Foundation of Jiangsu Province (BK2012566)

摘要

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.
- 进化优化 /
- 高维多目标优化 /
- 分解 /
- 并行 /
- Pareto占优
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.
- Evolutionary algorithm /
- many-objective optimization /
- decomposition /
- parallel /
- Pareto domination

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参考文献(29)

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基于目标分解的高维多目标并行进化优化方法

doi: 10.16383/j.aas.2015.c140832

作者简介:
巩敦卫中国矿业大学信息与电气工程学院教授.1999年在中国矿业大学获博士学位.主要研究方向为进化计算与应用.E-mail:dwgong@vip.163.com

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Parallel Many-objective Evolutionary Optimization Using Objectives Decomposition

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基于目标分解的高维多目标并行进化优化方法

doi: 10.16383/j.aas.2015.c140832

作者简介: 巩敦卫 中国矿业大学信息与电气工程学院教授.1999年在中国矿业大学获博士学位.主要研究方向为进化计算与应用.E-mail:dwgong@vip.163.com

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Parallel Many-objective Evolutionary Optimization Using Objectives Decomposition

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出版历程

目录

作者简介:
巩敦卫中国矿业大学信息与电气工程学院教授.1999年在中国矿业大学获博士学位.主要研究方向为进化计算与应用.E-mail:dwgong@vip.163.com