Abstract
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).
Highlights
In recent years, both in civilian and military environments, considerable attention has been paid to optimizing tasks and problems in a dynamically changing environment
The ant colony optimization (ACO)-simulated annealing (SA) algorithm was implemented in C++ programming language using MS Visual Studio integrated development environment
The dynamic traveling salesman problem (DTSP) benchmark instances are based on the well-known TSPLIB symmetric problems [47]
Summary
Both in civilian and military environments, considerable attention has been paid to optimizing tasks and problems in a dynamically changing environment. This attention is closely related to the growing increase in newly introduced technical means (e.g., unmanned systems), which are programmed to perform individual tasks by means of optimization techniques and algorithms. These techniques would make individual activities more efficient by finding a high-quality solution to the problem at hand. A simple example of a combinatorial DOP is the dynamic traveling salesman problem (DTSP). A metaheuristic approach for the DTSP is proposed and evaluated on a set of experiments
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