## 1 Introduction  
Bio-inspired optimization algorithms have proven that they are really good at numerical optimization and finding good results from a defined search space, no matter how many parameters they are looking for. In this paper, a comparison of most popular bio-inspired methods is presented in the problem of optimization of membership functions for a fuzzy controller. In this paper, three bio-inspired methods are used for a comparison in the optimization of a fuzzy system used as a controller of an autonomous mobile robot. These methods are selected because they use the same methodology for parameter adaptation, as these three methods use a fuzzy system with the same inputs, but with different outputs, because the outputs are the parameters to be dynamically adjusted over the iterations of each algorithm. PSO (Particle Swarm Optimization), inspired by collective intelligence, can handle complex problems and gives very good results, but lacks the ability to dynamically control its parameters, and a methodology for parameter adaptation is presented. The Bat Algorithm is a bio-inspired metaheuristic based on the characteristics of micro-bats, essentially echolocation. This echolocation is used by a bat to locate its prey; this algorithm was formulated in 2010 by [PERSON] in [1], and has since been used to solve global optimization problems. PID (Proportional-Integral-Derivative)-fuzzy controllers are tuned in [2; 3] using PSO and tested on an electrical DC drive benchmark and a laboratory micro air vehicle controller, respectively. A hybrid PSO and pattern search optimized PI-fuzzy controller is applied in [4] to the automatic generation control of multi-area power systems. The Gravitational Search Algorithm (GSA) is employed in [5; 6] for the optimal tuning of PI-fuzzy and PID-fuzzy controllers for DC servo systems and load frequency control in power systems. Charged System Search algorithms are applied in [7] to the optimal tuning of PI-fuzzy controllers for DC servo systems. Ant Colony Optimization is applied in [8; 9] to the optimal tuning of fuzzy controllers for robots and ball and beam systems. A hybridization of the algorithms was performed in [10]; the authors publish a hybridization between the PSO and GA algorithms to minimize the cost and materials required for the elaboration of a metal cylinder. The artificial bee colony algorithm (ABC) has been used to optimize this same problem [11]. The authors propose in [12] a novel intelligent technique for solutions in a biography system and, through a comparison, the authors conclude that the solutions of the proposed approach are better than the solutions available in the literature. In [13], the authors present a new GA-GSA hybrid algorithm to increase reliability and performance in the analysis of uncertain data and the intuitionistic fuzzy is used to determine the optimal parameters in the algorithms, intuitionistic fuzzy logic, is a powerful technique in bio-inspired algorithms. A new model for decision making is proposed in [14]; the objective of this new model is to reduce the level of uncertainty in the data to help decision making. This model uses fuzzy logic to eliminate the level of uncertainty; the use of these techniques have significantly improved timing and pressure by decision makers. In [15], with the objective of determine the level of membership of an object to another in numerical terms, a novel method that uses Type-2 fuzzy set (T2 FS) is proposed, and this method significantly improves the accuracy of membership of a data set. Recently the BCO algorithm has proven to be an efficient technique in solving complex problems, especially for fuzzy controllers; some current examples of research are the following: [PERSON] et al. [16], a new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers is presented; [PERSON] et al.