Clonal Selection Approach ith Mutations Based on Symmetric α-Stable Distributions for Non-stationary Optimization Tasks

摘要

Efficiency of two mutation operators applied in a clonal selection based optimization algorithm AHA for non-stationary tasks is investigated. In both operators traditional Gaussian random number generator was exchanged by α-stable random number generator and thus α became one of the parameters of the algorithm. Obtained results showed that appropriate tuning of the α parameter allows to outperform the results of algorithms with the traditional operators.