caa a22 4500 605470812 CHVBK 20210128100330.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00500-014-1480-9 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1480-9 A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment [Elektronische Daten] [Fangqing Gu, Hai-Lin Liu, Kay Tan] There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs. Springer-Verlag Berlin Heidelberg, 2014 Multiobjective optimization nationallicence Decomposition nationallicence Hybrid algorithm nationallicence Evolutionary algorithm nationallicence Gu Fangqing Guangdong University of Technology, Guangdong, China aut Liu Hai-Lin Guangdong University of Technology, Guangdong, China aut Tan Kay Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3249-3259 1432-7643 19:11<3249 2015 19 500 https://doi.org/10.1007/s00500-014-1480-9 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/s00500-014-1480-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Gu Fangqing Guangdong University of Technology, Guangdong, China aut NATIONALLICENCE 700 1- Liu Hai-Lin Guangdong University of Technology, Guangdong, China aut NATIONALLICENCE 700 1- Tan Kay Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3249-3259 1432-7643 19:11<3249 2015 19 500