Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization

In this paper, we present a new approach of hybrid simulated annealing method for minimizing multimodel functions called the simulated annealing heuristic pattern search (SAHPS) method. Two subsidiary methods are proposed to achieve the final form of the global search method SAHPS. First, we introduce the approximate descent direction (ADD) method, which is a derivative-free procedure with high ability of producing a descent direction. Then, the ADD method is combined with a pattern search method with direction pruning to construct the heuristic pattern search (HPS) method. The last method is hybridized with simulated annealing to obtain the SAHPS method. The experimental results through well-known test functions are shown to demonstrate the e ciency of the proposed method SAHPS.