【摘要】 蚁群算法广泛应用于求解组合优化问题,但基本蚁群算法与其它模拟进化算法存在进化速度慢,并易于陷入局部最小等缺陷,在此提出一种采用自适应选择和动态调整的进化策略,通过TSP问题的仿真表明,算法的性能明显得到改善,该方法不仅能够加快收敛速度,节省搜索时间,而且能够克服停滞行为的过早出现,有利于发现更好的解.这对于求解大规模的优化问题是十分有利的.更多还原

【Abstract】 Ant colony algorithm is a kind of simulated evolutionary algorithm. It is proposed by Italian scholar Macro Dorigo. It has many good features and has been widely applied to solving complicated combinatorial optimization problems. But there is much deficiency. Specially, its searching speed is slow, and it is easy to fall in the local best. In this paper, the performance of ant colony algorithm is improved by the adaptive selection and the dymanic readjust evolutional strategy. The simulation for TSP problem shows that the improved algorithm can find better paths at higher convergence speed. It is beneficial to solve the wide scale optimization problems.更多还原