进化算法
计算机科学
多目标优化
差异进化
数学优化
变量(数学)
进化计算
比例(比率)
最优化问题
算法
人工智能
数学
机器学习
量子力学
物理
数学分析
作者
Bin Cao,Jianwei Zhao,Zhihan Lv,Xin Liu
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2017-08-01
卷期号:13 (4): 2030-2038
被引量:79
标识
DOI:10.1109/tii.2017.2676000
摘要
A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.
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