Many real-life applications governed by discrete variables poss multiple local optimal solutions, which requires the utilization of global optimization tools find the best solution amongst them. The main difficulty in determining the best solution, or also known as the global solution, is to escape from the basins surrounding local minimums. To overcome this issue, an auxiliary function is introduced in discrete filled function method which turns the local minimizer of the original function become a maximizer. Then, an improved local minimum is found by minimizing the filled function, otherwise the edge of the feasible region is attained. Based on a discrete filled function method from the literature, we propose a modification particularly on the neighbourhood search to enhance its computational efficiency. Numerical results suggest that the proposed algorithm is efficient in solving large scale complex discrete optimization problems.
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