Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame
Küçük Resim Yok
Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inderscience Enterprises Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, a many-objective hybrid real-code population-based incremental learning and differential evolution algorithm (MnRPBILDE) is proposed based on the concept of objective function space reduction. The method is then implemented on real engineering design problems. The topology, shape and sizing design of a simplified automotive floor-frame structure are formulated and used as test problems. A variety of well-established multi-objective evolutionary algorithms (MOEAs) including the original version of MnRPBILDE are employed to solve the test problems while the results are compared based on hypervolume and C indicators. The results indicate that our proposed algorithm outperforms the other MOEAs. The proposed algorithm is effective and efficient for many-objective optimisations of a car floor-frame structure.
Açıklama
Anahtar Kelimeler
car floor-frame design, many-objective optimisation, population-based incremental learning, differential evolution, topology optimisation
Kaynak
International Journal Of Vehicle Design
WoS Q Değeri
Q4
Scopus Q Değeri
Q4
Cilt
73
Sayı
1-3
