Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 9559-9572 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 52 |
Issue number | 9 |
Online published | 17 Mar 2021 |
Publication status | Published - Sept 2022 |
Link(s)
DOI | DOI |
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Document Link | |
Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85101121543&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(72f8d4bd-82c9-4090-a74c-92c5427d973e).html |
Abstract
Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions.
Research Area(s)
- Constrained multiobjective optimization problems (CMOPs), constraint satisfaction, Convergence, evolutionary algorithm, Evolutionary computation, objective optimization, Optimization, Search problems, Sociology, Sorting, Statistics
Citation Format(s)
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization. / Tian, Ye; Zhang, Yajie; Su, Yansen et al.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 9, 09.2022, p. 9559-9572.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 9, 09.2022, p. 9559-9572.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review