Convergence Analysis of Cuckoo Search by Creating Markov Chain - 周晖 程亚乔 李丹美 徐晨 (东华大学学报（英文版）, 2016)
Cuckoo search（CS） has been used successfully for solving global optimization problems. From a theoretical point of view, the convergence of the CS is an important issue. In this paper, convergence analysis of CS was studied. The transition probability characteristics of the population to construct a Markov chain were analyzed. The homogeneity of the Markov chain was derived based on stochastic process theory. Then it was proved to be an absorbing state Markov chain. Consequently, the global convergence of CS was deduced based on conditions of convergence sequence and total probability formula, and the expected convergence time was given. Finally, a series of experiments were conducted. Experimental results were analyzed and it is observed that CS seems to perform better than PSO.