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.

Metaheuristic algorithms applied to NP-hard problems are proven effective techniques in the field of optimization to ensure a good result within an acceptable calculation time. However, finding a suitable technique for optimizing a complex problem is not an easy task since there are hundreds of methods. The majority of metaheuristic research is characterized by developing a new algorithm for a task or modifying or improving an existing technique. The reuse of metaheuristics is small compared to the fact that up to 30+ new procedures can be presented in the scientific community every year. Metaheuristic algorithms have already been grouped in countless ways, but not based on their components and structural elements, which are responsible for the basic optimization performance of the given method. The grouping of the problems to be solved is also not typical in terms of which method can be used to solve them effectively. This paper contributes to filling this gap by introducing a novel classification system for algorithms and problems in terms of variables or based on the type of task. Two main categories were distinguished for both logistic tasks and metaheuristic algorithms: discrete and continuous. Linking the nature of the variables between the task and the algorithm is a significant step forward in choosing an efficient metaheuristic with a high probability for a logistics problem. This can increase the efficiency of solving logistics problems and expand the use of the latest optimization techniques.