caa a22 4500 605469229 CHVBK 20210128100321.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s00500-014-1308-7 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1308-7 DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm [Elektronische Daten] [Alexandru-Ciprian Zăvoianu, Edwin Lughofer, Gerd Bramerdorfer, Wolfgang Amrhein, Erich Klement] 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. Springer-Verlag Berlin Heidelberg, 2014 Evolutionary computation nationallicence Hybrid multi-objective optimization nationallicence Coevolution nationallicence Adaptive allocation of fitness evaluations nationallicence Performance analysis methodology for MOOPs nationallicence Zăvoianu Alexandru-Ciprian Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut Lughofer Edwin Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut Bramerdorfer Gerd LCM, Linz Center of Mechatronics, Linz, Austria aut Amrhein Wolfgang LCM, Linz Center of Mechatronics, Linz, Austria aut Klement Erich Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3551-3569 1432-7643 19:12<3551 2015 19 500 https://doi.org/10.1007/s00500-014-1308-7 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1308-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zăvoianu Alexandru-Ciprian Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut NATIONALLICENCE 700 1- Lughofer Edwin Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut NATIONALLICENCE 700 1- Bramerdorfer Gerd LCM, Linz Center of Mechatronics, Linz, Austria aut NATIONALLICENCE 700 1- Amrhein Wolfgang LCM, Linz Center of Mechatronics, Linz, Austria aut NATIONALLICENCE 700 1- Klement Erich Department of Knowledge-based Mathematical Systems/Fuzzy Logic Laboratory Linz-Hagenberg, Johannes Kepler University of Linz, Linz, Austria aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3551-3569 1432-7643 19:12<3551 2015 19 500