A genetic ant colony optimization approach for concave cost transportation problemsac
Küçük Resim Yok
Tarih
2007
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The concave cost transportation problem (ccTP) is one of the practical distribution and logistics problems. The ccTP arises when the unit cost for transporting products decreases as the amount of products increases. Generally, these costs are modeled as nonlinear, especially concave. Since the ccTP is NP-hard, solving large-scale problems is time-consuming. In this paper, we propose a hybrid search algorithm based on genetic algorithms (GA) and ant colony optimization (ACO) to solve the ccTP. This algorithm, called h_GACO, is a GA supplemented with ACO in where ACO is implemented to exploit information stored in pheromone trails during genetic operations, i.e. crossover and mutation. The effectiveness of h_GACO is investigated comparing its results with those obtained by five different metaheuristic approaches given in the literature for the ccTP.
Açıklama
IEEE Congress on Evolutionary Computation -- SEP 25-28, 2007 -- Singapore, SINGAPORE
Anahtar Kelimeler
Kaynak
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
