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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Abstract 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.
AbstractList 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.
Author Qingfu Zhang
Xi Lin
Sam Kwong
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  surname: Sam Kwong
  fullname: Sam Kwong
  email: cssamk@cityu.edu.hk
  organization: Dept. of Comput. Sci., City Univ. of Hong Kong, Hong Kong, China
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Snippet This paper proposes a novel surrogate-model-based multi-objective evolutionary algorithm, which is called Multi-objective Bayesian Optimization Algorithm based...
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StartPage 1343
SubjectTerms Algorithm design and analysis
Bayes methods
Computational modeling
Gaussian processes
Kernel
Lead
Optimization
Title An efficient batch expensive multi-objective evolutionary algorithm based on Decomposition
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