A new stochastic search algorithm for global optimization based on mutation operator

We present a random restart heuristic for the global optimization problem that is based on the principles of mutation inspired by biology, it only uses the mutation operator to search the solution space. Combining local optimization by the mutation operator and random restart method in order to increase the reliability of finding the global optimum, the new algorithm can obtain satisfactory results in limited time. The superiority of this methodology over the conventional genetic algorithm is established on some problems of optimizing complex functions.