A Collaborative Resource Allocation Strategy for Decomposition-Based Multiobjective Evolutionary Algorithms
Autor: | Li Li, Qi Kang, MengChu Zhou, Xinyao Song |
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Rok vydání: | 2019 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization Process (engineering) MathematicsofComputing_NUMERICALANALYSIS Evolutionary algorithm 02 engineering and technology ComputingMethodologies_ARTIFICIALINTELLIGENCE Multi-objective optimization Evolutionary computation Computer Science Applications Human-Computer Interaction 020901 industrial engineering & automation Test case Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) Resource allocation 020201 artificial intelligence & image processing Resource management Electrical and Electronic Engineering Software |
Zdroj: | IEEE Transactions on Systems, Man, and Cybernetics: Systems. 49:2416-2423 |
ISSN: | 2168-2232 2168-2216 |
DOI: | 10.1109/tsmc.2018.2818175 |
Popis: | 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. |
Databáze: | OpenAIRE |
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