Simulation optimization using balanced explorative and exploitative search

We present a new random search method for solving simulation optimization problems. Our approach emphasizes the need for maintaining the right balance between exploration and exploitation during various stages of the search. Exploration represents global search for promising solutions within the entire feasible region, while exploitation involves local search of promising subregions. Preliminary numerical results are provided that show the performance of the method applied to solve deterministic and stochastic optimization problems.