Convergence analysis of a self-adaptive multi-objective evolutionary algorithm based on grids

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Colleges, School and Institutes


Evolutionary algorithms have been successfully applied to various multi-objective optimization problems. However, theoretical studies on multi-objective evolutionary algorithms, especially with self-adaption, are relatively scarce. This paper analyzes the convergence properties of a self-adaptive (mu + 1)-algorithm. The convergence of the algorithm is defined, and general convergence conditions are studied. Under these conditions, it is proven that the proposed self-adaptive (mu + 1)-algorithm converges in probability or almost surely to the Pareto-optimal front. (c) 2007 Elsevier B.V. All rights reserved.


Original languageEnglish
Pages (from-to)117-122
Number of pages6
JournalInformation Processing Letters
Issue number4
Publication statusPublished - 1 Nov 2007


  • evolutionary algorithms, multi-objective optimization, analysis of algorithms, convergence