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A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms
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DOI：10.1109/TSMC.2018.2818175.png)
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Abstract
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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.
Keywords:
Collaborative resource allocation (CRA)
decomposition
evolutionary algorithms
multiobjective optimization problem (MOP)
Pareto front (PF)
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