An integral approach for Geno-Simulated Annealing

Global optimization is the problem of finding the global optimum of any given function in a certain search space. Simulated Annealing (SA) and Genetic Algorithms (GA) are among the well-known techniques used for global optimization. Adjusting the parameters of SA such as the temperature schedule and the neighborhood range plays an important role in the performance of the algorithm. Furthermore, many studies in literature showed that the best values for SA parameters depend on the optimization problem. We introduce a novel hybrid approach that uses SA to solve an optimization problem and uses GA simultaneously to adapt the parameters of SA. This new approach is referred to as Geno-Simulated Annealing (GSA). It does not require any predefined values for the parameters of SA. To evaluate the performance of the proposed approach, we used seven well-known benchmark optimization functions. The obtained results indicate the superiority of the proposed approach as compared to a similar approach and to conventional SA.