Wang, Rui and Zhang, Qingfu and Zhang, Tao (2016) Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods. IEEE Transactions on Evolutionary Computation, 20 (6). pp. 821-837. DOI https://doi.org/10.1109/tevc.2016.2521175
Wang, Rui and Zhang, Qingfu and Zhang, Tao (2016) Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods. IEEE Transactions on Evolutionary Computation, 20 (6). pp. 821-837. DOI https://doi.org/10.1109/tevc.2016.2521175
Wang, Rui and Zhang, Qingfu and Zhang, Tao (2016) Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods. IEEE Transactions on Evolutionary Computation, 20 (6). pp. 821-837. DOI https://doi.org/10.1109/tevc.2016.2521175
Abstract
Decomposition-based algorithms have become increasingly popular for evolutionary multiobjective optimization. However, the effect of scalarizing methods used in these algorithms is still far from being well understood. This paper analyzes a family of frequently used scalarizing methods, the L<inf>p</inf> methods, and shows that the p value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pareto optimal front (PF) geometries. It demonstrates that an L<inf>p</inf> method that can maximize the search ability of a decomposition-based algorithm exists and guarantees that, given some weight, any solution along the PF can be found. Moreover, a simple yet effective method called Pareto adaptive scalarizing (PaS) approximation is proposed to approximate the optimal p value. In order to demonstrate the effectiveness of PaS, we incorporate PaS into a state-of-the-art decomposition-based algorithm, i.e., multiobjective evolutionary algorithm based on decomposition (MOEA/D), and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives. Experimental results demonstrate that the PaS is effective.
Item Type: | Article |
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Uncontrolled Keywords: | Decomposition; evolutionary computation; multiobjective evolutionary algorithm based on decomposition (MOEA/D); multiobjective optimization; scalarizing method |
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: | 14 Dec 2016 09:46 |
Last Modified: | 11 Jun 2025 13:02 |
URI: | http://repository.essex.ac.uk/id/eprint/18554 |