An Offline Multi-objective Optimization with Association Approximation to Reference Vectors
LI Rui
SUN Chaoli
ZHANG Guochen
Abstract：Many real-world engineering and science problems are computationally expensive multi-objective optimization problems,in which the evaluation of each candidate solution is time-consuming.Thus,only a small number of real objective evalua-tions are allowed.In this paper,an offline data-driven evolutionary algorithm is proposed for solving computationally expensive multi-objective optimization problems,which is expected to save the time of optimization.In the proposed method,the surrogate model is trained to approximate the convergence performance of a candidate solution.The reference vector that a candidate solution is associated and determined by the nearest sample of the candidate solution.And accordingly,it is expected to reduce the accumu-lated errors resulting from calculating the angle between the approximated objective values and the reference vector.The DTLZ test set is used to verify the effectiveness of the proposed method.The proposed method is compared with an offline data-driven optimiza-tion algorithm named MS-RV and three classic online data-driven optimization algorithms.Experimental results show that our pro-posed method can reduce the number of real evaluations without deteriorating the performance.
Keywords：computationally expensive multi-objective optimization problemssurrogate modelsoffline data-driven optimi-zationnearest neighbor estimation
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2577-2582 )