The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.

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