Metaheuristics in Logistics: Increasing the Efficiency of Algorithms by Defining Appropriate Parameter Settings

Metaheuristic algorithms are well-researched and popular techniques in the field of optimization, which can solve complex tasks with a large number of instances with acceptable quality. They are extremely problem- and parameter-sensitive methods, so the exact definition of the necessary data and the testing of the appropriate parameters fundamentally determine the efficiency and performance of an algorithm. This is a time-consuming and expensive task. In many cases, when applying a metaheuristic, it works properly with the variables of a given task and there is no specific interval where a given algorithm can still be effective. To increase efficiency and reduce costs, the authors defined a general parameter definition by applying the Ant Colony Optimization algorithm applicable to the simple Traveling Salesman Problem with the number of cities n=50, where for values of 30 = n = 50, the defined parameter setting structure can be properly applied based on the results. The proposed parameter setting structure can work effectively not only for the task presented in the paper, but also for any similar task within the defined interval. In the case of tasks of a similar size, it is not necessary to experiment with the parameters to achieve the appropriate result, thereby reducing the optimization time and improving efficiency. The presentation of the set parameter setting scenarios and the obtained results all contribute to reducing the optimization time in the field of logistics as well. All of this can also help facilitate the practical application of metaheuristics in solving NP-hard tasks.