A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms | IEEE Journals & Magazine | IEEE Xplore

A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms


Abstract:

Decomposition of a multiobjective optimization problem (MOP) into several simple multiobjective subproblems, named multiobjective evolutionary algorithm based on decompos...Show More

Abstract:

Decomposition of a multiobjective optimization problem (MOP) into several simple multiobjective subproblems, named multiobjective evolutionary algorithm based on decomposition (MOEA/D)-M2M, is a new version of multiobjective optimization-based decomposition. However, it fails to consider different contributions from each subproblem but treats them equally instead. This paper proposes a collaborative resource allocation (CRA) strategy for MOEA/D-M2M, named MOEA/D-CRA. It allocates computational resources dynamically to subproblems based on their contributions. In addition, an external archive is utilized to obtain the collaborative information about contributions during a search process. Experimental results indicate that MOEA/D-CRA outperforms its peers on 61% of the test cases in terms of three metrics, thereby validating the effectiveness of the proposed CRA strategy in solving MOPs.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 49, Issue: 12, December 2019)
Page(s): 2416 - 2423
Date of Publication: 09 May 2018

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