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Soft Computing
Erschienen in: Soft Computing 3/2010

01.02.2010 | Original Paper

Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

Erschienen in: Soft Computing | Ausgabe 3/2010

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Abstract

A self-adaptive differential evolution algorithm incorporate Pareto dominance to solve multi-objective optimization problems is presented. The proposed approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. The experiments were performed using eighteen benchmark test functions. The experiment results show that, compared with three other multi-objective optimization evolutionary algorithms, the proposed MOSADE is able to find better spread of solutions with better convergence to the Pareto front and preserve the diversity of Pareto optimal solutions more efficiently.
Nächster Artikel Automatic detection of trends in time-stamped sequences: an evolutionary approach

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Metadaten
Titel
Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure
Publikationsdatum
01.02.2010
Erschienen in
Soft Computing / Ausgabe 3/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-008-0394-9

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