###### Abstract  
The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system's behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However, the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems. fuzzy logic controller; meta-heuristics; cuckoo search algorithm; magnetic levitation system +
Footnote †: journal: Applied  
an optimization tool can be used even if there is no guarantee that the optimal solution can be obtained, techniques such as trial and error methods may help to find \"acceptable solutions\" [1; 2]. In this regard, researchers have focused their efforts on developing algorithms capable of solving these problems in a more efficient manner. Real-life applications found in engineering, business activities, industrial designs, statistics, decision making, optimal control, machine learning, etc., require optimization of (a set of) objective functions that can be anything, for example, minimization of energy consumption and costs, maximization of profit, output, performance, and efficiency, of a global optimization problem [3; 4]. Moreover, optimization can be defined as a field of science in which the values of parameters of the problem are explored under specified conditions (constraints) in order to find those particular values which enable an objective or set of objective functions to generate the minimum or maximum value [5; 6]. In general, an optimization problem can be formulated in terms of a correctly defined cost/objective function in which all the objectives of optimization related to the problem are involved. Moreover, if any predefined constraints are to be used with respect of the parameters of the related problem, these should be considered during the design of the optimization problem and its corresponding cost/objective function [7]. During the last decades, meta-heuristic nature-inspired algorithms have been given special attention due to their great capability in solving optimization problems in a wide range of applications such as operation and control of electric power systems [8; 9; 10; 11; 12], chemical processes [13; 14; 15], job scheduling [16; 17; 18], vehicle routing [19; 20], autonomous vehicles control [21], mobile networking [22; 23], multi-objective optimization [24; 25; 26], image processing [27], etc. Some of these algorithms found in the literature are cuckoo search, bat algorithm, particle swarm optimization, firefly algorithm, ant algorithms, genetic algorithm, differential evolution, etc.