On the convergence rates of clonal selection algorithm

It is complicated and important to study the covergence rates of clonal selection algorithms in the field of artificial immune computation. However, there are few results about it. By integrating chaos mechanism and niche technique, an improved clonal selection algorithm (ICSA) is proposed based on the clonal selection principle. The algorithm not only maintains better population diversity than the classical clonal selection algorithm, but also converges to the global optimal solution rapidly. In this paper, the classical homogeneous Markov chain analyse is replaced by a new pure probability theory, and from the definition of strong convergence in probability, the convergence rate of the ICSA is analyzed under some conditions, and a method of estimating the convergence rate of the ICSA is obtained.