A stopping criterion for multi-objective optimization evolutionary algorithms

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 (...

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Bibliographic Details
Published in:Information sciences 2016-11, Vol.367-368, p.700-718
Main Authors: Martí, Luis, García, Jesús, Berlanga, Antonio, Molina, José M.
Format: Article
Language:English
Subjects:
Borides
Convergence detection
Criteria
Design optimization
Evolutionary algorithms
Indicators
Kalman filters
Magnesium compounds
Multi-objective evolutionary algorithms
Multi-objective optimization
Optimization
Progress indicators
Stagnation
Stopping criteria
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Summary: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.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2016.07.025