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

Qi Kang, Xinyao Song, Mengchu Zhou, Li Li

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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.

Original languageEnglish (US)
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
StateAccepted/In press - May 9 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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