Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

doi:10.1109/JAS.2022.105875

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization
	Ye Tian; Haowen Chen; Haiping Ma; Xingyi Zhang; Kay Chen Tan; Yaochu Jin
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:10 页码:1801-1817
关键词	Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming
ISSN号	2329-9266
DOI	10.1109/JAS.2022.105875
英文摘要	Large-scale multi-objective optimization problems (LSMOPs) pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search spaces. While evolutionary algorithms are good at solving small-scale multi-objective optimization problems, they are criticized for low efficiency in converging to the optimums of LSMOPs. By contrast, mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems, but they have difficulties in finding diverse solutions for LSMOPs. Currently, how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains unexplored. In this paper, a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient method. On the one hand, conjugate gradients and differential evolution are used to update different decision variables of a set of solutions, where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto front. On the other hand, objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions, and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its parent. In comparison with state-of-the-art evolutionary algorithms, mathematical programming methods, and hybrid algorithms, the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49710]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Ye Tian,Haowen Chen,Haiping Ma,et al. Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(10):1801-1817.
APA	Ye Tian,Haowen Chen,Haiping Ma,Xingyi Zhang,Kay Chen Tan,&Yaochu Jin.(2022).Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization.IEEE/CAA Journal of Automatica Sinica,9(10),1801-1817.
MLA	Ye Tian,et al."Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization".IEEE/CAA Journal of Automatica Sinica 9.10(2022):1801-1817.