The expansion of control systems in many applications, leads to various control techniques. Due to acceptable and analyzable approximate description of a system and formulate human knowledge, the use of fuzzy logic and fuzzy control systems acquired a satisfactory place in the various control systems. After the appearance of practical applications of fuzzy controllers, concentration of designers and researchers to this study field was increased. However, according to the theory of Professor Lotffi Zadeh, designing of fuzzy controller is based on expert knowledge, regularization of fuzzy sets and finding the if-then rules. This issue for simple problems that can gain input-output data in case of the existence of expert knowledge can be done with trial and error procedure. but most of controlling problems have their own special complexity and experts cannot interpret all of issues raised in the controlling process in order to be beneficiary. So researchers have been looking for a solution through which we can reduce the amount of human intervention in the design of fuzzy controller and some works performed in this field and several methods has been introduced. One of them is the use of heuristic optimization methods for the design of this type of controller with these methods. Very complex problems that have different parameters and several limits can be easily considered, so efficient, sustainable and robust controller with minimal human intervention is achieved. In this thesis, fuzzy systems, Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms are introduced, and in the following, this set of algorithms has been used to optimal regularization of Mamdani fuzzy controller for automatic regulation of generator voltage and nonlinear inverted pendulum systems. Also, an optimal fuzzy load frequency controller (FLFC) based artificial bee colony algorithm is developed for solution of the load frequency control problem in an interconnected power system. In the proposed method, to improve the design performance and achieving the desired level of robust performance, exact tuning of membership functions and fuzzy control rules is very important. Thus, to reduce the fuzzy system design effort and improve fuzzy rule base performance, the membership functions and fuzzy rules are designed simultaneously and automatically by artificial bee colony algorithm, that proven its superior capabilities, such as faster convergence and better global minimum achievement. Then, load frequency control system for generator has been explained. This newly developed control strategy combines the advantages of ABC algorithm and fuzzy logic controller (FLC) and provides some advantages such as flexible controller with simple structure, self-tuning of FLC parameters during real-time operation and easy algorithm. To illustrate the capability of the proposed approach, the numerical results are .
