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

Document Type

Article

Publication Date

12-1-2019

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.

Identifier

85059046755 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Systems

External Full Text Location

https://doi.org/10.1109/TSMC.2018.2818175

e-ISSN

21682232

ISSN

21682216

First Page

2416

Last Page

2423

Issue

12

Volume

49

Grant

51775385

Fund Ref

National Natural Science Foundation of China

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