Title :
On the convergence rate method of an Improved Clonal Selection Algorithm
Author_Institution :
Sch. of Electron. Eng., Huaihai Inst. of Technol., Lianyungang, China
Abstract :
It is complicated and important to study the covergence rate of clonal selection algorithms in the field of artificial immune computation. However, there are few theoretical studies for it. The classical homogeneous Markov chain analyse is replaced by a new pure probability method, and from the definition of strong convergence in probability, the convergence rate of an Improved Clonal Selection Algorithm(ICSA) is analyzed under some conditions, and a method of estimating the convergence rate of the ICSA is obtained. The simulation results of multi-modal function optimization illustrate the validity of convergence rate method.
Keywords :
Markov processes; artificial immune systems; convergence; probability; artificial immune computation; classical homogeneous Markov chain; convergence rate method; improved clonal selection algorithm; multimodal function optimization; probability method; Algorithm design and analysis; Convergence; Electronic mail; Evolutionary computation; Immune system; Markov processes; Optimization; Chaos; Clonal Selection Principle; Convergence; Strong Convergence in Probability;
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768
