Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem
1
Department of Intelligence Support, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic
2
Department of Military Geography and Meteorology, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 884; https://doi.org/10.3390/e22080884
Received: 21 July 2020
/
Revised: 6 August 2020
/
Accepted: 9 August 2020
/
Published: 12 August 2020
(This article belongs to the Special Issue Artificial Intelligence and Computational Methods in the Modeling of Complex Systems)
The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).
View Full-Text
Keywords:
dynamic traveling salesman problem; combinatorial dynamic optimization problem; ant colony optimization; simulated annealing; hybridization; metaheuristic algorithm
▼
Show Figures
Figure 1
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
MDPI and ACS Style
Stodola, P.; Michenka, K.; Nohel, J.; Rybanský, M. Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem. Entropy 2020, 22, 884. https://doi.org/10.3390/e22080884
AMA Style
Stodola P, Michenka K, Nohel J, Rybanský M. Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem. Entropy. 2020; 22(8):884. https://doi.org/10.3390/e22080884
Chicago/Turabian StyleStodola, Petr, Karel Michenka, Jan Nohel, and Marian Rybanský. 2020. "Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem" Entropy 22, no. 8: 884. https://doi.org/10.3390/e22080884
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
