Derivative-free simulated annealing and deflecting function technique for global optimization

摘要

A hybrid descent method based on simulated annealing (SA) algorithm and one modifying function technique, named deflecting function method, for global optimization is proposed. Unlike some previously proposed algorithms, the designed SA algorithm is executed repeatedly on the transformed function with respect to one prior-obtained local minimum instead of on the original objective function. Meanwhile, large scale searches at the beginning stages and small scale detections in the last stages are adopted. The global convergence is proved. Simulation demonstrates that the new method utilizes the obtained information effectively, so the convergence is significantly sped up and the success rate is greatly improved, compared with other existing methods. As an experimental result, how to combine SA and the deflecting function technique can make the new method more effective is discussed.