Enhancing MOEA/D with Escape Mechanisms
- Authors
- Derbel, Bilel; Pruvost, Geoffrey; Hong, Byung-Woo
- Issue Date
- Aug-2021
- Publisher
- IEEE
- Keywords
- Multi-objective optimization; decomposition
- Citation
- 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), pp 1163 - 1170
- Pages
- 8
- Journal Title
- 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021)
- Start Page
- 1163
- End Page
- 1170
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52197
- DOI
- 10.1109/CEC45853.2021.9504957
- ISSN
- 0000-0000
- Abstract
- In this paper, we investigate the design of escape mechanisms within the state-of-the-art decomposition-based evolutionary multi-objective MOEA/D framework. We propose to track the number of improvements made with respect to the single-objective sub-problems defined by decomposition. This allows us to compute an estimated sub-problem improvement probability which serves as an activation signal for some solution perturbation mechanism to occur. We report the benefits of such an approach by conducting a comprehensive experimental analysis on a broad range of combinatorial bi-objective bit-string landscapes with variable dimensions and ruggedness. Our empirical findings provide evidence on the effectiveness of the proposed escape mechanism and its ability in providing substantial improvement over conventional MOEA/D. Besides, we provide a detailed analysis of parameters impact and anytime behavior in order to better highlight the strength of the proposed techniques as a function of available budget and problem characteristics.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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