A New Differential Evolution Algorithm for Minimax Optimization in Robust Design

Xin Qiu, Jian Xin Xu, Yinghao Xu, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

70 Citations (Scopus)

Abstract

Minimax optimization, which is actively involved in numerous robust design problems, aims at pursuing the solutions with best worst-case performances. Although considerable research has been devoted to the development of minimax optimization algorithms, there still exist several fundamental limitations for existing approaches, e.g., restriction on problem types, excessively high computational cost, and low optimization efficiency. To address these issues, a minimax differential evolution algorithm is proposed in this paper. First, a novel bottom-boosting scheme enables the algorithm to identify the promising solutions in a reliable yet efficient manner. After that, a partial-regeneration strategy together with a new mutation operator contribute to an in-depth exploration over solution space. Finally, a proper integration of these newly proposed mechanisms leads to an algorithmic structure that can appropriately handle various types of problems. Empirical comparison with seven famous methods demonstrates the statistical superiority of the proposed algorithm. Successful applications in two open problems of robust design further validate the effectiveness of the new approach.

Original languageEnglish
Pages (from-to)1355-1368
Number of pages14
JournalIEEE Transactions on Cybernetics
Volume48
Issue number5
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Keywords

  • Differential evolution (DE)
  • evolutionary algorithm (EA)
  • minimax optimization problem
  • robust design

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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