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Structural and Multidisciplinary Optimization

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31-10-2020 | Research Paper

An efficient multi-objective optimization method based on the adaptive approximation model of the radial basis function

Authors: Xin Liu, Xiang Liu, Zhenhua Zhou, Lin Hu

Published in: Structural and Multidisciplinary Optimization | Issue 3/2021

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Abstract

Considering the high computational cost caused by solving multi-objective optimization (MOO) problems, an efficient multi-objective optimization method based on the adaptive approximation model is developed. Firstly, the Latin hypercube design (LHD) is employed for obtaining the initial sample points. Secondly, initial approximation models of objective functions and constraints are established by using the radial basis function (RBF). For ensuring the accuracy of the approximation models, the reverse shape parameter analysis method (RSPAM) is proposed to obtain improved approximation models. Thirdly, the micro multi-objective genetic algorithm (μMOGA) is adopted to solve the Pareto optimal set and the local-densifying approximation method is also applied to strengthen the ability of solving accurate Pareto optimal sets. Finally, the effectiveness and practicability of the proposed method is demonstrated by two numerical examples and two engineering examples.

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Title: An efficient multi-objective optimization method based on the adaptive approximation model of the radial basis function
Authors: Xin Liu
Xiang Liu
Zhenhua Zhou
Lin Hu
Publication date: 31-10-2020
Publisher: Springer Berlin Heidelberg
Published in: Structural and Multidisciplinary Optimization / Issue 3/2021
Print ISSN: 1615-147X
Electronic ISSN: 1615-1488
DOI: https://doi.org/10.1007/s00158-020-02766-2

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