Abstract
For solving large-scale multiobjective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this article, we propose to solve LSMOPs via a multivariation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multivariation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with two to three objectives and 500-5000 variables. The experimental results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multiobjective optimization.
Original language | English |
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Pages (from-to) | 248-262 |
Number of pages | 15 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 26 |
Issue number | 2 |
Early online date | 13 Oct 2021 |
DOIs | |
Publication status | Published - Apr 2022 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1997-2012 IEEE.
Funding
This work was supported in part by the National Key Research and Development Project, Ministry of Science and Technology, China, under Grant 2018AAA0101301; in part by the National Natural Science Foundation of China (NSFC) under Grant 61876162 and Grant 61876025; in part by the Research Grants Council of the Hong Kong SAR under Grant PolyU11202418 and Grant PolyU11209219; and in part by the Venture and Innovation Support Program for Chongqing Overseas Returnees under Grant cx2018044 and Grant cx2019020.
Keywords
- Evolutionary multitasking
- large-scale optimization
- multifactorial optimization (MFO)
- multiobjective optimization