Adaptive Dynamic Clone Selection Strategy for Optimization

Abstract

Based on the Antibody Clonal Selection Theory of immunology, an adaptive dynamic clone select algorithm is put forward. The new algorithm is intended to integrate the local searching with the global and the probability evolution searching with the stochastic searching. Compared with other algorithms, the new algorithm prevents prematurely more effectively and has high convergence speed. Numeric experiments of function optimization indicate that the new algorithm is effective and useful.