EAL, Institut für elektrische Antriebe und Leistungselektronik

Institut für elektrische Antriebe und Leistungselektronik

JKU, Johannes Kepler Universität Linz

Sprache: DE

Publikations Einzelansicht

DECMO2: A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm

Artikel

Autor(en): Alexandru-Ciprian Zavoianu, Edwin Lughofer, Gerd Bramerdorfer, Wolfgang Amrhein und Erich Peter Klement
Journal: Soft Computing A Fusion of Foundations, Methodologies and Applications ISSN 3551–3569 Published online: 4 June 2014 © Springer-Verlag Berlin Heidelberg 2014
Jahr: 2015
Band: 19
Ausgabe: 12
Seite(n): 3551 - 3569, 18 pages
Datei / URL: Soft Comput (2015) 19:3551-3569 DOI 10.1007/s00500-014-1308-7
Zusammenfassung: We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes ofmulti-objective evolutionary algorithms (MOEAs): classical approaches that use Pareto-based selection for survival criteria, approaches that rely on differential evolution, and decomposition-based strategies. A key part of our hybrid evolutionary approach lies in the proposed fitness sharing mechanism that is able to smoothly transfer information between the coevolved subpopulations without negatively impacting the specific evolutionary process behavior that characterizes each subpopulation. The proposed MOEA also features an adaptive allocation of fitness evaluations between the coevolved populations to increase robustness and favor the evolutionary search strategy that proves more successful for solving the MOOP at hand. Apart from the new evolutionary algorithm, this paper also contains the description of a new hypervolume and racing-based methodology aimed at providing practitioners from the field ofmulti-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets.

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