Open AccessAn Expensive Multi-Objective Optimization Algorithm Based on Decision Space Compression
Author(s) -
Haosen Liu,
Fangqing Gu,
Yiu-ming Cheung
Publication year - 2021
Publication title -
international journal of pattern recognition and artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 55
eISSN - 1793-6381
pISSN - 0218-0014
DOI - 10.1142/s0218001421590394
Subject(s) - surrogate model , computer science , mathematical optimization , optimization problem , algorithm , space (punctuation) , compression (physics) , artificial intelligence , machine learning , mathematics , materials science , composite material , operating system
Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space.