Direct Search Simulated Annealing (DSSA) Method

In this method, we give a new approach of hybrid direct search methods with metaheuristics of simulated annealing for finding a global minimum of a nonlinear function with continuous variables. First, we suggest a Simple Direct Search (SDS) method, which comes from some ideas of other well known direct search methods. Since our goal is to find global minima and the SDS method is still a local search method, we hybridize it with the standard simulated annealing to design a new method, called Simplex Simulated Annealing (SSA) method, which is expected to have some ability to look for a global minimum. To obtain faster convergence, we first accelerate the cooling schedule in SSA, and in the final stage, we apply Kelley's modification of the Nelder-Mead method on the best solutions found by the accelerated SSA method to improve the final results. We refer to this last method as the Direct Search Simulated Annealing (DSSA) method.

Source Reference: A. Hedar and M. Fukushima, Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization, Optimization Methods and Software, 17 (2002) 891-912. [pdf]

Source Codes: MATLAB codes for DSSA are downloadable here DSSA_ver_0.1.