􀁽 Ant colony algorithm is developed through the simulation of ant foraging out of a new heuristic algorithm 􀁱 population-based collaboration and learning 􀁯 that the algorithm has been successfully resolved the problem, such as × ≥ ° 􀁱 a variety of combinatorial optimization problems proposed in this paper a kinds of adaptive pheromone-based ant colony algorithm to improve the algorithm based on artificial ants 􀁱 obtained by solution of 􀁯 dynamically adjust the pheromone on the path which makes the algorithm 􀁯 jumped off the local optimal solution 􀁱 results obtained through simulation experiments show that 􀁯 The algorithm for improved ant colony algorithm has good effect

自适应调整信息素的蚁群算法.pdf

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