Optimization of a fuzzy controller for autonomous robot navigation using a new competitive multi-metaheuristic model

This paper describes a proposed competitive multi-metaheuristic optimization model for the optimal design of membership function parameters of a fuzzy system that controls the navigation of an autonomous mobile robot following a desired trajectory. The main contribution is the new competitive multi-metaheuristic optimization model, which is formed by an architecture consisting of the firefly algorithm, wind-driven optimization, drone squadron optimization, and stochastic fractal search. The proposed method has enable obtaining good results in the optimization of fuzzy controllers, which outperform other metaheuristics in the literature. In the competition, each of the methods is tested until finding the best one in generating an effective parameter vector for the optimization of fuzzy controller membership functions. The main contribution of this article is the use of four metaheuristics working in a competitive way, to find the best vector of data generated with the optimal values to successfully adjust the membership functions of the fuzzy controller. The resulting winning method proves in this way that it is the best among the chosen algorithms to solve this kind of control optimization problem.

Automated summary

This study introduces a competitive multi-metaheuristic optimization model, which compares four different metaheuristics to find the best method for optimizing the membership functions of fuzzy controllers.