Publication: A stopping criterion for multi-objective optimization evolutionary algorithms
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2016-11-01
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Elsevier
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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.
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stopping criteria, convergence detection, stagnation, progress indicators, multi-objective evolutionary algorithms, multi-objective optimization, kalman filters
Bibliographic citation
Martí, L., García, J., Berlanga, A., Molina, J.M. (2016). A stopping criterion for multi-objective optimization evolutionary algorithms. Information Sciences, 367-368, pp. 700-718