An efficient batch expensive multi-objective evolutionary algorithm based on Decomposition

This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-objective problem is decomposed into several subproblems which will be solved simultan...

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Veröffentlicht in:	2017 IEEE Congress on Evolutionary Computation (CEC) S. 1343 - 1349
Hauptverfasser:	Xi Lin, Qingfu Zhang, Sam Kwong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.06.2017
Schlagworte:	Algorithm design and analysis Bayes methods Computational modeling Gaussian processes Kernel Lead Optimization
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Zusammenfassung:	This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based on Decomposition (MOBO/D). In this algorithm, a multi-objective problem is decomposed into several subproblems which will be solved simultaneously. MOBO/D builds Gaussian process model for each objective to learn the optimization surface, and defines utility function for each subproblem to guide the searching process. At each generation, MOEA/D algorithm is called to locate a set of candidate solutions which maximize all utility functions respectively, and a subset of those candidate solutions is selected for parallel batch evaluation. Experimental study on different test instances validates that MOBO/D can efficiently solve expensive multi-objective problems in parallel. The performance of MOBO/D is also better than several classical expensive optimization methods.
DOI:	10.1109/CEC.2017.7969460