The effect of mutation rate to Artificial Immune System algorithm optimization in solving engineering Gear Train problem

Artificial Immune System (AIS) algorithm has a great interest for the researcher recently because AIS have a big room for improvement since AIS does not have a fix algorithm template. This paper analyzes the effect of impact of mutation rate compare to the standard AIS algorithm. The purpose of this stimulation was done is to understand deeply the affected step in the standard AIS algorithm. It can increase the efficiency to obtain the final best solution for the particular solving problem. By comparing the optimization result in the Gear Train using Evolutionary [1], HAIS [2], GeneAS [3] and GPSO [4], it is observed that changing the mutation rate on standard AIS algorithm can achieve a better solution.