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Sensors 2019, 19(8), 1837; https://doi.org/10.3390/s19081837

Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem

1
Department of Information System, College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia
2
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 21 March 2019 / Revised: 7 April 2019 / Accepted: 14 April 2019 / Published: 17 April 2019

Abstract

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time. View Full-Text
Keywords: traveling salesman problem (TSP); ant colony optimization (ACO); flying ant colony optimization (FACO); dynamic flying ant colony optimization (DFACO) traveling salesman problem (TSP); ant colony optimization (ACO); flying ant colony optimization (FACO); dynamic flying ant colony optimization (DFACO)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dahan, F.; El Hindi, K.; Mathkour, H.; AlSalman, H. Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem. Sensors 2019, 19, 1837.

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