Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy

Jan Nohel¹  Petr Stodola¹  Karel Michenka¹  Marian Rybanský² 

[1] Department of Intelligence Support, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic;Department of Military Geography and Meteorology, University of Defence, Kounicova 65, 662 10 Brno, Czech Republic;

关键词: dynamic traveling salesman problem;    combinatorial dynamic optimization problem;    ant colony optimization;    simulated annealing;    hybridization;    metaheuristic algorithm;   

DOI  :  10.3390/e22080884

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown