algorithm in [8; 9], and [PERSON] et al. [10] also describes applications of this method, giving good results to solve optimization problems, finding routes or paths that lead to find the best solution in a specific search space, which is based on how the ants find the shortest path from their anthill to their food. Another example is the genetic algorithm, which is also a widely used metaheuristic and is inspired by the biological evolution developed by [PERSON] in [11; 12], where the algorithm is based on the idea of the survival of the fittest, going from generation in generation. In this case, each generation has a best individual, which is considered the one that has a better performance to find a good solution to a problem. Some examples of the use of the aforementioned algorithms are the following. In [13], a new hybrid Gravitational Search Algorithm-Genetic Algorithm (GSA-GA) algorithm is presented for the restriction of nonlinear optimization problems with mixed variables. A hydrothermal system is also developed for storage by pumping that incorporates solar units using particle swarm optimization as described in [14]. Another example of the use of metaheuristics is the hybrid technique called Particle Swarm Optimization-Genetic Algorithm PSO-GA to solve restricted optimization problems [15]; for multi-objective problems, particle swarm optimization has been used as explained in [16]. In addition, [PERSON] [17] illustrates a problem of structural engineering design optimization with limitations of nonlinear resources using the artificial bee colony algorithm. In addition, in the control area, metaheuristics are combined with fuzzy logic to solve optimization problems, such as the optimization of membership functions to improve the behavior of a fuzzy controller. For example, in [18], the firefly algorithm is used for the optimization of a fuzzy controller of a standalone mobile robot, which performs dynamic adjustment of the randomness parameter of the method. Also in [19], fuzzy dynamic adjustment is performed for optimization using galactic swarm optimization, among others, and [20; 21; 22] also describe applications of the method. This article proposes the development of a multi-metaheuristic competitive model using the firefly algorithm (FA), the wind-driven particle optimization algorithm (WDO), and the drone squad optimization (DSO) algorithm, where each of them competes with each other to demonstrate which of them is the best at solving optimization problems. The question to answer in this work is: could a multi-metaheuristic competitive method improve the performance of a fuzzy control? The algorithms chosen for this model were selected because they proved to give good results in optimization problems according to the state of the art and experimentation. The main contribution in this work is the development of a competitive model that will serve as a methodology to solve optimization problems. The motivation in this work arises with the concern to solve the need that exists regarding having an optimization methodology that helps us evaluate different optimization algorithms by means of competitiveness between methods, and thus solve the problems of optimization that arise in an application area. The objective is to find the method that provides the best results in a particular optimization problem. Preliminary tests were performed with unimodal, multimodal, multimodal benchmark functions of fixed dimension, in addition to the optimization of a fuzzy controller of a robot that follows the line. This paper is organized as follows: Section 2 describes the background of the methods used, Section 3 presents the proposed methodology and shows the problems to be solved, Section 4 describes the results, Section 5 describes the discussion, and finally Section 6 show the conclusions.