TY - GEN
T1 - An efficient batch expensive multi-objective evolutionary algorithm based on Decomposition
AU - LIN, Xi
AU - ZHANG, Qingfu
AU - KWONG, Sam
N1 - 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 ).
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85027842432&partnerID=8YFLogxK
U2 - 10.1109/CEC.2017.7969460
DO - 10.1109/CEC.2017.7969460
M3 - Conference paper (refereed)
SP - 1343
EP - 1349
BT - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
ER -