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DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm.

Zăvoianu, Alexandru-Ciprian; Lughofer, Edwin; Bramerdorfer, Gerd; Amrhein, Wolfgang; Klement, Erich Peter

Authors

Edwin Lughofer

Gerd Bramerdorfer

Wolfgang Amrhein

Erich Peter Klement



Abstract

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 of multi-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 of multi-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets.

Journal Article Type Article
Publication Date Dec 31, 2015
Journal Soft Computing
Print ISSN 1432-7643
Electronic ISSN 1433-7479
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 19
Issue 12
Pages 3551-3569
Institution Citation ZĂVOIANU, A.-C., LUGHOFER, E., BRAMERDORFER, G., AMRHEIN, W. and KLEMENT, E.P. 2015. DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. Soft computing [online], 19(12), pages 3551-3569. Available from: https://doi.org/10.1007/s00500-014-1308-7
DOI https://doi.org/10.1007/s00500-014-1308-7
Keywords Evolutionary computation; Hybrid multi-objective optimization; Coevolution; Adaptive allocation of fitness evaluations; Performance analysis methodology for MOOPs

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