A Fuzzy Meta Model for Adjusting Ant Colony System Parameters

Metaheuristic algorithms have become an important choice for solving complex optimization problems which are difficult to solve by conventional methods. But, like many other metaheuristic algorithms, ant colony system (ACS) has the problem of parameters setting. In the last few years, different approaches have been proposed to deal whit this problem. Recently the use of fuzzy logic in dynamic parameters adaptation of metaheuristic algorithms is gaining a considerable interest from the researchers. In this paper, a meta model for modifying the parameters of ACS during runtime based on fuzzy logic concept is presented. The main idea is to study the effect of modifying all the parameters of the ACS on the same time on its performance. To compare the efficiency of the proposed approaches, they were applied to a set of traveling salesman problem instances. Also, a comparison with the standard ACS and some literature results are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.