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.
In this paper we propose two memetic algorithms for the university course timetabling problem. First by using graph coloring heuristics a new crossover method is proposed. Then to hybridize the genetic algorithm, a local search method with a mechanism similar to hill climbing is defined. The proposed memetic algorithms are applied on some datasets and their performance are compared with each other. The obtained results demonstrate that the first memetic algorithm has a better performance than the second one. Also a saturation degree heuristic is utilized in the crossover which improves the performances of the memetic algorithms. Comparison of the obtained results with the results reported in the literature demonstrates the comparability of our proposed algorithms with already existing algorithms.
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