An Ant Colony Optimization Memorizing Better Solutions (ACO-MBS) for Traveling Salesman Problem
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Ant Colony Optimization (ACO) is a population-based meta-heuristic method that mimics the foraging behavior of the ant colony in real life. The pheromone approach as the highlight method of the algorithm is the most effective factor in determining the moving of ants. Therefore, the problem of tuning the pheromone trail is an important topic for ACO that deserves attention. In this paper, a novel method which memorizes the solution costs and updates the pheromone trail according to the memorized costs is introduced for updating the pheromone trail in ACO. The performance of the proposed method was simulated on the Travelling Salesman Problem (TSP) and compared with the versions of ACO algorithm. © 2019 IEEE.
Açıklama
3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 -- 11 October 2019 through 13 October 2019 -- Ankara -- 156063
Anahtar Kelimeler
ant colony optimization, pheromone updating, traveling salesman problem
Kaynak
3rd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2019 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
