A clustering-based surrogate-assisted evolutionary algorithm is proposed for computationally expensive multi-objective optimization problems. Under the framework of MOEA/D, the population is partitioned into several clusters, and the population subsets are formed via the neighbourhood of the weights. Then the radial basis function surrogate-assisted differential evolution algorithm is used to generate new solution points from the formed subsets, and the population is updated using the generated new solution. Numerical experiments have been undertaken on 7 DTLZ test problems, and the computational results indicate that the proposed evolutionary algorithm has advantages over the newly developed multi-objective neighborhood regression optimization (MONRO) algorithm.
BAI Fusheng, CHEN Jiaoling
. A clustering-based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization[J]. Operations Research Transactions, 2022
, 26(4)
: 31
-42
.
DOI: 10.15960/j.cnki.issn.1007-6093.2022.04.003
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