Convergence of the simulated annealing algorithm for continuous global optimization

A class of simulated annealing algorithms for continuous global optimizatio n is considered in this paper. The global convergence property is analyzed with respect to the objective value sequence and the minimum objective valu e sequence induced by simulated annealing algorithms. The convergence analy sis provides the appropriate conditions on both the generation probability density function and the temperature updating function. Different forms of temperature updating functions are obtained with respect to different kinds of generation probability density functions, leading to different types of simulated annealing algorithms which all guarantee the convergence to the global optimum.