A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An intelligent testing system embedded with an ant-colony-optimization-based test composition method
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Continuous function optimization using hybrid ant colony approach with orthogonal design scheme
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Study of parametric relation in ant colony optimization approach to traveling salesman problem
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Parametric study for an ant algorithm applied to water distribution system optimization
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the ant colony system (ACS) algorithm, ants build tours mainly depending on the pheromone information on edges. The parameter settings of pheromone updating in ACS have direct effect on the performance of the algorithm. However, it is a difficult task to choose the proper pheromone decay parameters α and ρ for ACS. This paper presents a novel version of ACS algorithm for obtaining self-adaptive parameters control in pheromone updating rules. The proposed adaptive ACS (AACS) algorithm employs Average Tour Similarity (ATS) as an indicator of the optimization state in the ACS. Instead of using fixed values of α and ρ, the values of α and ρ are adaptively adjusted according to the normalized value of ATS. The AACS algorithm has been applied to optimize several benchmark TSP instances. The solution quality and the convergence rate are favorably compared with the ACS using fixed values of α and ρ. Experimental results confirm that our proposed method is effective and outperforms the conventional ACS.