Abstract: Artificial Immune Systems (AIS) has generated great interest among researchers as the algorithm is able to improve local searching ability and efficiency. However, the rate of convergence for AIS in finding the global minima is rather slow as compare to other Evolutionary Algorithms. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used effectively in solving complicated optimization problems, but they tend to converge prematurely at the local minima. In this study, the Hybrid AIS (HAIS) is proposed by combining the good features of AIS and PSO in order to reduce this shortcoming. By comparing the optimization results of the mathematical functions and the engineering problem using GA, AIS and HAIS, it is observed that HAIS achieved better performances in terms of accuracy, convergence rate and stability.