Adaptive Exchanging Strategies in Parallel Ant Colony Algorithm

Two strategies for information exchange between processors in parallel ant colony algorithm are presented. Theses strategies can make each processor choose other processors to communicate and to update the pheromone adaptively. A strategy is also presented to adjust the time interval of information exchange adaptively according to the distribution of the solutions so as to keep balance between the convergence speed and the diversity of the solutions. The adaptive parallel ant colony algorithm (APACA) based on these strategies adaptively updates the pheromone according to the equilibrium of the pheromone distribution in each information exchange so as to avoid the precocity and local convergence. These strategies are applied to the traveling salesman problem on the massive parallel processors (MPP) Dawn 2000. Experimental results show that the algorithm has higher convergence speed, speedup and efficiency than other parallel ant algorithms.

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