Study the effect of high dimensional objective functions on multi-objective evolutionary algorithms
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Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computing Machinery
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Multi-objective Evolutionary Algorithms (MOEAs) have been widely studied by many researchers and they have been used to solve different real-world applications that have more than one objective function. However, most MOEAs work well only when the number of objective functions is small such as two or three. The performance of MOEAs starts degrading significantly when number of objective functions increases. Therefore, there is increasing importance for studying and analyzing the effect of increasing the number of objective functions on the performance of current multi objective evolutionary algorithms. In this paper, the performance of three state-of-the-art multi objective evolutionary algorithms is investigated when increasing the number of objective functions. The tested algorithms are analyzed using test DTLZ test suit. The results show that SMPSO and NSGA-II algorithms are the best two algorithms for high number of objective functions. In addition, the results show that the running time of SMPSO and GDE3 algorithms was effected and increased much when the number of objective functions is large. © Copyright 2018 ACM.
Açıklama
International Association of Researchers (IARES)
4th International Conference on Engineering and MIS, ICEMIS 2018 -- 19 June 2018 through 20 June 2018 -- -- 138526
4th International Conference on Engineering and MIS, ICEMIS 2018 -- 19 June 2018 through 20 June 2018 -- -- 138526
Anahtar Kelimeler
Differential Evolution Algorithm, Evolutionary Algorithms, Multi-Objective Problems, NSGA-II, PSO
Kaynak
ACM International Conference Proceeding Series
WoS Q Değeri
N/A
Scopus Q Değeri
Q4
