Turning high-dimensional optimization into computationally expensive optimization

Peng Yang, Ke Tang, Xin Yao

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)
246 Downloads (Pure)


Divide-and-Conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple lowdimensional sub-problems, and tackling them separately. Nevertheless, the dimensionality mismatch between the original problem and sub-problems makes it non-trivial to precisely assess the quality of a candidate solution to a sub-problem, which has been a major hurdle for applying the idea of DC to non-separable highdimensional optimization problems. In this paper, we suggest that searching a good solution to a sub-problem 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 4 representative compared algorithms increase with the problem size on the CEC2010 large scale global optimization benchmark. The weakness of SEE is also analysed in the empirical studies.
Original languageEnglish
JournalIEEE Transactions on Evolutionary Computation
Early online date22 Feb 2017
Publication statusE-pub ahead of print - 22 Feb 2017


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