A New Metaheuristic Approach for Stabilizing the Solution Quality of Simulated Annealing and Applications

In this work we describe a new metaheuristical approach for global optimization that is based on the simulated annealing algorithm. Within this new approach a preoptimization step with a Greedy strategy is performed to compute an initial solution for the intrinsic iterations of the simulated annealing algorithm. Furthermore, the probability distribution which is used for generation of a new solution candidate is adjusted in a way that candidates in the optimization direction are chosen with a higher probability. We will empirically show the superiority of our metaheuristic for three different combinatorical optimization problems of practical relevance in comparison to other standard techniques that are usually applied to compute solutions of the corresponding problems. Moreover, the dependence of the solution quality on the choice of specific input parameters for simulated annealing can be reduced significantly with our metaheuristic. Finally, we will consider a fourth complex problem class, where our metaheuristic is unable to compute significantly better solutions in comparison to simple local optimization strategies. Consequently, for this problem class local optimization is sufficient for practical applications to determine an adequately good solution near the global optimum.