An Expensive Multi-objective Optimization Algorithm Based on Decision Space Compression

Haosen Liu, Fangqing Gu*, Yiu Ming Cheung

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

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.
Original languageEnglish
Article number2159039
Number of pages19
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume35
Issue number7
DOIs
Publication statusPublished - 7 Jul 2021

User-Defined Keywords

  • Decision space compression
  • expensive multi-objective
  • evolutionary algorithm

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