Application of Artificial Immune Networks in Continuous Function Optimizations

Abstract

This paper deals with the application of artificial immune networks in continuous function optimizations. The performance of the immunological algorithms is analyzed using the Optimization Algorithm Toolkit. It is shown that the CLIGA algorithm has, by far, the fastest convergence and the best score - in terms of the number of required iterations, for the analyzed continuous function. Also, based on the test results, it was concluded, that the lowest total number of iterations for the defined run time was achieved with the opt-IA algorithm, followed by the CLONALG and CLIGA algorithms.