Average convergence rate estimation of clonal selection algorithm

Abstract

Considering that average convergence rate estimation of clonal selection algorithms is a difficult problem and is still in its infancy, this article researches the convergence rate of an elitist clonal selection algorithm. It derives the best individual transition probability matrix from the directional transition probability of best individuals in algorithm populations and constructs matrix norms that meet certain requirements to resolve difficulties in calculating the matrix caused by large algorithm populations in practical applications, thereby proposing a simple and effective method of estimating average convergence rate of the algorithm. In addition, simulation experiments are performed to validate universality and validity of the estimation method.