This paper introduces Particle Swarm Optimization (PSO) Algorithm, Adaptive Weighted PSO (AWPSO) Algorithm and Genetic Algorithm (GA) to determine the optimal proportional-integral derivative (PID Controller Parameters) of a position control for a typical hydraulic servo system (HSS). The performance indices that were used in the optimization are integral absolute error (IAE), integral squared error (ISE), and integral time absolute error (ITAE). The proposed controller is implemented on a simulation model of a hydraulic system in real time to know the best method of adjusting the controller. The PSO method allows better adjustment in terms of stabilization time, maximum overshoot and undershoot. Compared with the GA and AWPSO results, the PSO method was found to be more effective and robust in improving the step response of a position control for hydraulic systems. A mathematical model of a hydraulic servo system is presented. This model includes the most relevant dynamic and nonlinear effects. The model describes the behavior of a REXROTH servovalve and includes nonlinearities of friction forces, valve dynamics, oil compressibility and the influence of load. Servo-hydraulic systems (HSS) play an important role in the industrial field because they can produce high torque and large forces at high speeds. Applications for hydraulic servo systems include manipulators, material testing machines, fatigue testing, paper machines, ships, injection molding machines, robotics and aerospace. The dynamics of hydraulic systems are highly nonlinear due to change in direction of valve opening, friction, etc. (Sohl and Bobrow, 1999). In hydraulic control systems, one of the main objectives of control is to obtain a desired and satisfactory response middle of paper......g," Computers and Chemical Engineering 26.6:903-908Saad, MS, Jamaluddin , H. and Darus, IZM, 2012, “Implementing PID Controller Tuning Using Differential Evolution and Genetic Algorithms”, International Journal of Innovative Computing Information And Control, 8(11), 7761-7779. Sirouspour, Mohammad R. and SE Salcudean, 2000, “On nonlinear control of hydraulic servo systems,” in Proceedings of the IEEE International Conference on Robotics and Automation ICRA'00. Sivanandam, S.N., Visalakshi, P. and Bhuvaneswari, A., 2007. Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia, 4(3), pp. 95-106. Garett A. and James E. Bobrow, (1999), “Experiments and Simulations on Nonlinear Control of a Hydraulic Servo System,” IEEE Transactions onControl Systems Technology, 7(2), p.. 238-247