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Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms

Zhou, A and Zhang, Q (2016) 'Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms.' IEEE Transactions on Evolutionary Computation, 20 (1). 52 - 64. ISSN 1089-778X

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Abstract

© 1997-2012 IEEE. Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem into a set of scalar objective subproblems and solve them in a collaborative way. A naïve way to distribute computational effort is to treat all the subproblems equally and assign the same computational resource to each subproblem. This paper proposes a generalized resource allocation (GRA) strategy for decomposition-based MOEAs by using a probability of improvement vector. Each subproblem is chosen to invest according to this vector. An offline measurement and an online measurement of the subproblem hardness are used to maintain and update this vector. Utility functions are proposed and studied for implementing a reasonable and stable online resource allocation strategy. Extensive experimental studies on the proposed GRA strategy have been conducted.

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: 15 Mar 2016 15:33
Last Modified: 30 Jan 2019 16:20
URI: http://repository.essex.ac.uk/id/eprint/16262

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