期刊
ACTA POLYTECHNICA HUNGARICA
卷 19, 期 7, 页码 153-164出版社
BUDAPEST TECH
关键词
artificial immune networks; Optimization Algorithm Toolkit; continuous function optimization; performance
This paper discusses the application of artificial immune networks in continuous function optimizations, and analyzes the performance of immunological algorithms. It was found that the CLIGA algorithm has the fastest convergence and best score, while the opt-IA algorithm achieved the lowest total number of iterations within the defined run time.
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.
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