| Title | Evolutionary multiobjective optimization via efficient sampling-based offspring generation |
| Author | |
| Corresponding Author | Cheng, Ran |
| Publication Years | 2023-02-01
|
| DOI | |
| Source Title | |
| ISSN | 2199-4536
|
| EISSN | 2198-6053
|
| Abstract | With the rising number of large-scale multiobjective optimization problems from academia and industries, some evolutionary algorithms (EAs) with different decision variable handling strategies have been proposed in recent years. They mainly emphasize the balance between convergence enhancement and diversity maintenance for multiobjective optimization but ignore the local search tailored for large-scale optimization. Consequently, most existing EAs can hardly obtain the global or local optima. To address this issue, we propose an efficient sampling-based offspring generation method for large-scale multiobjective optimization, where convergence enhancement and diversity maintenance, together with ad hoc local search, are considered. First, the decision variables are dynamically classified into two types for solving large-scale decision space in a divide-and-conquer manner. Then, a convergence-related sampling strategy is designed to handle those decision variables related to convergence enhancement. Two additional sampling strategies are proposed for diversity maintenance and local search, respectively. Experimental results on problems with up to 5000 decision variables have indicated the effectiveness of the algorithm in large-scale multiobjective optimization. |
| Keywords | |
| URL | [Source Record] |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | Corresponding
|
| Funding Project | National Natural Science Foundation of China["U20A20306","61906081"]
; National Key Research and Development Program of China[2022YFB2403803]
|
| WOS Research Area | Computer Science
|
| WOS Subject | Computer Science, Artificial Intelligence
|
| WOS Accession No | WOS:000938057800002
|
| Publisher | |
| Data Source | Web of Science
|
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/502115 |
| Department | Department of Computer Science and Engineering |
| Affiliation | 1.Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China 2.Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automation, Wuhan 430074, Peoples R China 3.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain inspired Intelligent, Shenzhen 518055, Peoples R China 4.Bielefeld Univ, Chair Nat Inspired Comp & Engn, D-33615 Bielefeld, Germany |
| Corresponding Author Affilication | Department of Computer Science and Engineering |
| Recommended Citation GB/T 7714 |
He, Cheng,Li, Lianghao,Cheng, Ran,et al. Evolutionary multiobjective optimization via efficient sampling-based offspring generation[J]. Complex & Intelligent Systems,2023.
|
| APA |
He, Cheng,Li, Lianghao,Cheng, Ran,&Jin, Yaochu.(2023).Evolutionary multiobjective optimization via efficient sampling-based offspring generation.Complex & Intelligent Systems.
|
| MLA |
He, Cheng,et al."Evolutionary multiobjective optimization via efficient sampling-based offspring generation".Complex & Intelligent Systems (2023).
|
| Files in This Item: | There are no files associated with this item. | |||||
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