Adaptive parallel ant colony optimization

In this paper an adaptive parallel ant colony optimization is developed, We propose two different strategies for information exchange between the processors: selection based on sorting and on distance, which make each processor choose a partner to communicate and update the pheromone according to the partner's pheromone. In order to increase the ability of search and avoid early convergence, we also propose a method of adjusting the time interval of information exchange adaptively according to the convergence factor of each processor. Experimental results based on traveling salesman problem on the massive parallel processors (MPP) Dawn 2000 demonstrate the proposed APACO are superior to the classical ant colony optimization.