Please use this identifier to cite or link to this item:
Title: Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties
Authors: Li, Hui
Deb, Kalyanmoy
Zhang, Qingfu
Suganthan, Ponnuthurai Nagaratnam
Chen, Lei
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Li, H., Deb, K., Zhang, Q., Suganthan, P. N. & Chen, L. (2019). Comparison between MOEA/D and NSGA-III on a set of many and multi-objective benchmark problems with challenging difficulties. Swarm and Evolutionary Computation, 46, 104-117.
Journal: Swarm and Evolutionary Computation
Abstract: Currently, evolutionary multiobjective optimization (EMO) algorithms have been successfully used to find a good approximation of many-objective optimization problems (MaOPs). To measure the performance of EMO algorithms, many benchmark multiobjective test problems have been constructed. Among them, DTLZ and WFG are two representative test suites with the scalability to the number of variables and objectives. It should be pointed out that MaOPs can be more challenging if they are involved with difficult problem features, such as objective scalability, complicated Pareto set, bias, disconnection, or degeneracy. In this paper, a set of ten new test problems with above-mentioned difficulties are constructed. Some experimental results on these test problems found by two popular EMO algorithms, i.e., MOEA/D and NSGA-III, are reported and analyzed. Moreover, the performance of these two EMO algorithms with different population sizes on these test problems are also studied.
ISSN: 2210-6502
DOI: 10.1016/j.swevo.2019.02.003
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Page view(s)

Updated on Dec 5, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.