A stopping criterion for multi-objective optimization evolutionary algorithms Articles uri icon

publication date

  • noviembre 2016

start page

  • 700

end page

  • 718

volume

  • 367

international standard serial number (ISSN)

  • 0020-0255

electronic international standard serial number (EISSN)

  • 1872-6291

abstract

  • This paper puts forward a comprehensive study of the design of global stopping criteria for multi-objective optimization. In this study we propose a global stopping criterion, which is terms as MGBM after the authors surnames. MGBM combines a novel progress indicator, called mutual domination rate (MDR) indicator, with a simplified Kalman filter, which is used for evidence-gathering purposes. The MDR indicator, which is also introduced, is a special-purpose progress indicator designed for the purpose of stopping a multi-objective optimization. As part of the paper we describe the criterion from a theoretical perspective and examine its performance on a number of test problems. We also compare this method with similar approaches to the issue. The results of these experiments suggest that MGBM is a valid and accurate approach. (C) 2016 Elsevier Inc. All rights reserved.

keywords

  • Stopping criteria; Convergence detection; Stagnation; Progress indicators; Multi-objective evolutionary algorithms; Multi-objective optimization; Kalman filters