Adaptive ant colony algorithm based on cloud model
Z Liu, J Jiang, Y Yang, S Wang - 2015 IEEE International …, 2015 - ieeexplore.ieee.org
Z Liu, J Jiang, Y Yang, S Wang
2015 IEEE International Conference on Information and Automation, 2015•ieeexplore.ieee.orgTo overcome the slow convergence and local optimum of ant colony algorithm, the cloud
model theory is adopted to regulate reasonably the randomness of the ant colony algorithm.
In this paper, several adaptive strategies are proposed for the parameters of the ant colony
algorithm and the cloud model, and for the optimum path determination. Meanwhile, the
evaluation algorithm of pheromone distribution is proposed. Simulation results for multiple
TSP validate the efficiency and stability of the proposed algorithm.
model theory is adopted to regulate reasonably the randomness of the ant colony algorithm.
In this paper, several adaptive strategies are proposed for the parameters of the ant colony
algorithm and the cloud model, and for the optimum path determination. Meanwhile, the
evaluation algorithm of pheromone distribution is proposed. Simulation results for multiple
TSP validate the efficiency and stability of the proposed algorithm.
To overcome the slow convergence and local optimum of ant colony algorithm, the cloud model theory is adopted to regulate reasonably the randomness of the ant colony algorithm. In this paper, several adaptive strategies are proposed for the parameters of the ant colony algorithm and the cloud model, and for the optimum path determination. Meanwhile, the evaluation algorithm of pheromone distribution is proposed. Simulation results for multiple TSP validate the efficiency and stability of the proposed algorithm.
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