Wang, Bing-Chuan and Li, Han-Xiong and Zhang, Qingfu and Wang, Yong (2021) Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization. IEEE Transactions on Systems Man and Cybernetics: Systems, 51 (1). pp. 574-587. DOI https://doi.org/10.1109/tsmc.2018.2876335
Wang, Bing-Chuan and Li, Han-Xiong and Zhang, Qingfu and Wang, Yong (2021) Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization. IEEE Transactions on Systems Man and Cybernetics: Systems, 51 (1). pp. 574-587. DOI https://doi.org/10.1109/tsmc.2018.2876335
Wang, Bing-Chuan and Li, Han-Xiong and Zhang, Qingfu and Wang, Yong (2021) Decomposition-Based Multiobjective Optimization for Constrained Evolutionary Optimization. IEEE Transactions on Systems Man and Cybernetics: Systems, 51 (1). pp. 574-587. DOI https://doi.org/10.1109/tsmc.2018.2876335
Abstract
Pareto dominance-based multiobjective optimization has been successfully applied to constrained evolutionary optimization during the last two decades. However, as another famous multiobjective optimization framework, decomposition-based multiobjective optimization has not received sufficient attention from constrained evolutionary optimization. In this paper, we make use of decomposition-based multiobjective optimization to solve constrained optimization problems (COPs). In our method, first of all, a COP is transformed into a biobjective optimization problem (BOP). Afterward, the transformed BOP is decomposed into a number of scalar optimization subproblems. After generating an offspring for each subproblem by differential evolution, the weighted sum method is utilized for selection. In addition, to make decomposition-based multiobjective optimization suit the characteristics of constrained evolutionary optimization, weight vectors are elaborately adjusted. Moreover, for some extremely complicated COPs, a restart strategy is introduced to help the population jump out of a local optimum in the infeasible region. Extensive experiments on three sets of benchmark test functions, namely, 24 test functions from IEEE CEC2006, 36 test functions from IEEE CEC2010, and 56 test functions from IEEE CEC2017, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods.
Item Type: | Article |
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Uncontrolled Keywords: | Constrained optimization problems (COPs); decomposition; evolutionary algorithms (EAs); multiobjective optimization; Pareto dominance |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 28 Nov 2018 12:45 |
Last Modified: | 30 Oct 2024 20:47 |
URI: | http://repository.essex.ac.uk/id/eprint/23540 |
Available files
Filename: 08536901.pdf