An efficient two-phase ant colony optimization algorithm for the closest string problem

Authors:
Hoang Xuan Huan;Dong Do Duc;Nguyen Manh Ha
Affiliations:
University of Engineering and Technology, VNU, Hanoi, Vietnam;University of Engineering and Technology, VNU, Hanoi, Vietnam;University of Engineering and Technology, VNU, Hanoi, Vietnam
Venue:
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Year:
2012

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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.