Abstract: To settle the contradictory between convergence speed and precocity and stagnation in ant colony algorithm,an ant colony algorithm,which is based on adaptive selection of the paths and dynamic updating of pheromone,is presented.By dynamically adjusting the strategy of selection of the paths and the strategy of the trail information updating according to the distribution of the solutions,the algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.Experimental results on traveling salesman problem show that the method presented in this paper has a better global searching ability,higher convergence speed and solution diversity than that of classical ant colony algorithm.

摘要： 提出了路径相似度的概念,并根据较优可行解与最优解的相似度,来进行路径选择和信息素更新,以求能更快加速收敛和防止早熟、停滞现象。该算法根据截之间的相似度,自适应地调整路径选择策略和信息量更新策略。基于旅行商问题的实验验证了算法比一般蚁群算法具有更好的全局搜索能力、收敛速度和解的多样性。