An improved artificial immune system based on antibody remainder method for mathematical function optimization

Abstract

Artificial immune system (AIS) is one of the nature-inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Remainder-CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single objective functions.