Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization
文献类型：期刊论文
| 作者 | Ye Tian ; Haowen Chen; Haiping Ma; Xingyi Zhang; Kay Chen Tan; Yaochu Jin
|
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 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. |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。