An immune clonal algorithm based on the probability of cyclic mutation

Based on the traditional immune clonal algorithm, we suggest a new immune clonal algorithm with cycle mutation. This algorithm can enhance the convergence speed, effectively overcome the premature convergence, and well resolve similar complex problems of multidimensional function optimization. Its performances exhibit little effect from the initial distribution of population. In comparison with the simple clone algorithm and the self-adapting genetic algorithm, this algorithm shows a higher speed in computation and a superior ability and stability in the global and local search for optimal solutions in space.