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Applying graph-based differential grouping for multiobjective large-scale optimization
Swarm and Evolutionary Computation ( IF 10 ) Pub Date : 2019-12-13 , DOI: 10.1016/j.swevo.2019.100626
Bin Cao , Jianwei Zhao , Yu Gu , Yingbiao Ling , Xiaoliang Ma

An increasing number of multiobjective large-scale optimization problems (MOLSOPs) are emerging. Optimization based on variable grouping and cooperative coevolution is a good way to address MOLSOPs, but few attempts have been made to decompose the variables in MOLSOPs. In this paper, we propose multiobjective graph-based differential grouping with shift (mogDG-shift) to decompose the large number of variables in an MOLSOP. We analyze the variable properties, then detect the interactions among variables, and finally group the variables based on their properties and interactions. We modify the decision variable analyses (DVA) in the multiobjective evolutionary algorithm based on decision variable analyses (MOEA/DVA), extend graph-based differential grouping (gDG) to MOLSOPs, and test the method on many MOLSOPs. The experimental results show that mogDG-shift can achieve 100% grouping accuracy for LSMOP and DTLZ as well as almost all WFG instances, which are much better than DVA. We further combine mogDG-shift with two representative multiobjective evolutionary algorithms: the multiobjective evolutionary algorithm based on decomposition (MOEA/D) and the non-dominated sorting genetic algorithm II (NSGA-II). Compared with the original algorithms, the algorithms combined with mogDG-shift show improved optimization performance.



中文翻译:

基于图的差分分组在多目标大规模优化中的应用

越来越多的多目标大规模优化问题(MOLSOP)出现了。基于变量分组和协同协进化的优化是解决MOLSOP的好方法,但是很少有人尝试分解MOLSOP中的变量。在本文中,我们提出了基于多目标图的带偏移的微分分组(mogDG-shift),以分解MOLSOP中的大量变量。我们分析变量属性,然后检测变量之间的相互作用,最后根据变量的属性和相互作用对变量进行分组。我们在基于决策变量分析(MOEA / DVA)的多目标进化算法中修改了决策变量分析(DVA),将基于图的差分分组(gDG)扩展到了MOLSOP,并在许多MOLSOP上测试了该方法。LSMOP和DTLZ以及几乎所有WFG实例的分组精度为,这要比DVA好得多。我们进一步将mogDG-shift与两种代表性的多目标进化算法相结合:基于分解的多目标进化算法(MOEA / D)和非支配排序遗传算法II(NSGA-II)。与原始算法相比,与mogDG-shift相结合的算法具有更高的优化性能。

更新日期:2019-12-13
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