Applied Mathematics & Information Sciences

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This paper proposes a novel discrete version of Differential Evolution (NBDE) algorithm to solve combinatorial optimization problems with binary variables. A new binary mutation rule is introduced derived from the table of the basic DE mutation strategy and the value of scaling factor F is 1. The eight different combinations of the three randomly selected individuals using binary encoding are deduced. The developed mutation operator enables NBDE to explore and exploit the search space efficiently and effectively which are verified in applications to discrete optimization problems. Numerical experiments and comparisons on One-Max problem and Knapsack problem with two different sizes demonstrate that NBDE outperforms other existing algorithms in terms of final solution quality, search process efficiency and robustness.

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