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


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10 Citations (Scopus)


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 sub problem 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 MOBOID can efficiently solve expensive multi-objective problems in parallel. The performance of MOBOID is also better than several classical expensive optimization methods.
Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation (CEC) Proceedings
Number of pages7
ISBN (Electronic)9781509046010
ISBN (Print)9781509046027
Publication statusPublished - Jul 2017
Externally publishedYes
Event2017 IEEE Congress on Evolutionary Computation (CEC) - Donostia-San Sebastián, Spain
Duration: 5 Jun 20178 Jun 2017


Conference2017 IEEE Congress on Evolutionary Computation (CEC)
CityDonostia-San Sebastián

Bibliographical note

This work was supported by the National Natural Science Foundation of China under Grants 61473241, Hong Kong RGC General Research Fund(GRF) 9042038 (CityU 11205314), and a grant from ANR/RCC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityUlOlIl6 ).


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