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

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

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.

Citation

ZAVOIANU, 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

• Journal Article Type: Article
• Acceptance Date: Jun 4, 2014
• Online Publication Date: Jun 4, 2014
• Publication Date: Dec 31, 2015
• Deposit Date: Jan 27, 2020
• Publicly Available Date: Jan 27, 2020
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 12
• Pages: 3551-3569
• 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
• Public URL: https://rgu-repository.worktribe.com/output/816441