An Ant Colony Optimization Memorizing Better Solutions (ACO-MBS) for Traveling Salesman Problem

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Tarih

2019

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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

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N/A

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