Abstract
Given a finite set S of strings of length m, the task of finding a string t that minimizes the Hamming distance from t to S, has wide applications. This paper presents a two-phase Ant Colony Optimization (ACO) algorithm for the problem. The first phase uses the Smooth Max-Min (SMMAS) rule to update pheromone trails. The second phase is a memetic algorithm that uses ACO method to generate a population of solutions in each iteration, and a local search technique on the two best solutions. The efficiency of our algorithm has been evaluated by comparing to the Ant-CSP algorithm.
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Huan, H.X., Duc, D.D., Ha, N.M. (2012). An Efficient Two-Phase Ant Colony Optimization Algorithm for the Closest String Problem. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_19
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DOI: https://doi.org/10.1007/978-3-642-34859-4_19
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