COMBINED STRATEGY OF IMPROVED SIMULATED ANNEALING AND GENETIC ALGORITHM FOR INVERSE PROBLEM

A combined strategy of an improved Simulated Annealing(SA) algorithm a nd genetic algorithm is presented in this paper, with the goal of redu cing the computational expenses. The improvements made on SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of Metropolis process. Th e proposed strategy has both the advantage of SA algorithm, the abilit y to avoid being trapped in a local optimum, and that of genetic algor ithm, the ability to use the information about the searched states for next iteration. A practical application on geometry optimization of p ole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the nu mbers of iterations used by executing the combined strategy are only a bout 75% of those by executing basic SA algorithm.