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Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm

Wang, L and Zhang, Q and Zhou, A and Gong, M and Jiao, L (2016) 'Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm.' IEEE Transactions on Evolutionary Computation, 20 (3). 475 - 480. ISSN 1089-778X

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Abstract

© 2015 IEEE. A decomposition approach decomposes a multiobjective optimization problem into a number of scalar objective optimization subproblems. It plays a key role in decomposition-based multiobjective evolutionary algorithms. However, many widely used decomposition approaches, originally proposed for mathematical programming algorithms, may not be very suitable for evolutionary algorithms. To help decomposition-based multiobjective evolutionary algorithms balance the population diversity and convergence in an appropriate manner, this letter proposes to impose some constraints on the subproblems. Experiments have been conducted to demonstrate that our proposed constrained decomposition approach works well on most test instances. We further propose a strategy for adaptively adjusting constraints by using information collected from the search. Experimental results show that it can significantly improve the algorithm performance.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 12 Jul 2016 09:27
Last Modified: 15 Nov 2018 18:33
URI: http://repository.essex.ac.uk/id/eprint/17232

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