Turning High-Dimensional Optimization into Computationally Expensive Optimization

Peng YANG, Ke TANG, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

69 Citations (Scopus)

Abstract

Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple low-dimensional subproblems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and subproblems makes it nontrivial to precisely assess the quality of a candidate solution to a subproblem, which has been a major hurdle for applying the idea of DC to nonseparable high-dimensional optimization problems. In this paper, we suggest that searching a good solution to a subproblem can be viewed as a computationally expensive problem and can be addressed with the aid of meta-models. As a result, a novel approach, namely self-evaluation evolution (SEE) is proposed. Empirical studies have shown the advantages of SEE over four representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analyzed in the empirical studies. © 1997-2012 IEEE.
Original languageEnglish
Article number7862192
Pages (from-to)143-156
Number of pages14
JournalIEEE Transactions on Evolutionary Computation
Volume22
Issue number1
Early online date22 Feb 2017
DOIs
Publication statusPublished - Feb 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61329302 and Grant 61672478, in part by EPSRC under Grant EP/K001523/1 and Grant EP/J017515/1, in part by the Royal Society Newton Advanced Fellowship under Grant NA150123, and in part by SUSTech. The work of X. Yao was supported by the Royal Society Wolfson Research Merit Award.

Keywords

  • Computationally expensive optimization
  • divide-and-conquer (DC)
  • evolutionary algorithms (EAs)
  • high-dimensional optimization

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