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Biased Multiobjective Optimization and Decomposition Algorithm

Li, H and Zhang, Q and Deng, J (2017) 'Biased Multiobjective Optimization and Decomposition Algorithm.' IEEE Transactions on Cybernetics, 47 (1). 52 - 66. ISSN 2168-2267

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Abstract

© 2016 IEEE. The bias feature is a major factor that makes a multiobjective optimization problem (MOP) difficult for multiobjective evolutionary algorithms (MOEAs). To deal with this problem feature, an algorithm should carefully balance between exploration and exploitation. The decomposition-based MOEA decomposes an MOP into a number of single objective subproblems and solves them in a collaborative manner. Single objective optimizers can be easily used in this algorithm framework. Covariance matrix adaptation evolution strategy (CMA-ES) has proven to be able to strike good balance between the exploration and the exploitation of search space. This paper proposes a scheme to use both differential evolution (DE) and covariance matrix adaptation in the MOEA based on decomposition. In this scheme, single objective optimization problems are clustered into several groups. To reduce the computational overhead, only one subproblem from each group is selected to optimize by CMA-ES while other subproblems are optimized by DE. When an evolution strategy procedure meets some stopping criteria, it will be reinitialized and used for solving another subproblem in the same group. A set of new multiobjective test problems with bias features are constructed in this paper. Extensive experimental studies show that our proposed algorithm is suitable for dealing with problems with biases.

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: 14 Dec 2016 10:00
Last Modified: 30 Jan 2019 16:20
URI: http://repository.essex.ac.uk/id/eprint/18553

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