陶利民, 郭俊恩. 带有近邻选择策略和遗传算子的蚁群算法[J]. 微电子学与计算机, 2010, 27(4): 179-181,185.
引用本文: 陶利民, 郭俊恩. 带有近邻选择策略和遗传算子的蚁群算法[J]. 微电子学与计算机, 2010, 27(4): 179-181,185.
TAO Li-min, GUO Jun-en. Ant Colony Optimization Algorithm Based on the Nearest Neighbor Node Choosing Strategy and Genetic Operator[J]. Microelectronics & Computer, 2010, 27(4): 179-181,185.
Citation: TAO Li-min, GUO Jun-en. Ant Colony Optimization Algorithm Based on the Nearest Neighbor Node Choosing Strategy and Genetic Operator[J]. Microelectronics & Computer, 2010, 27(4): 179-181,185.

带有近邻选择策略和遗传算子的蚁群算法

Ant Colony Optimization Algorithm Based on the Nearest Neighbor Node Choosing Strategy and Genetic Operator

  • 摘要: 针对蚁群算法的不足, 文中提出一种带有近邻节点选择策略和遗传算子的蚁群算法.在算法执行时, 每只蚂蚁选择下一个城市采用近邻选择策略, 在每轮循环结束时, 对较优个体进行交叉运算的操作方法, 以期提高蚁群算法的收敛速度.实验结果表明该算法是有效的.

     

    Abstract: To aim at the lacks, an improved ACO is presented. This algorithm is an ACO having the nearest neighbor node choosing strategy and genetic operator. The ACO’s convergence speed is expected to be raised by means of using the nearest neighbor node choosing strategy when choosing the next city to move, and making crossover operator among better results at the end of each iteration. The experiment results indicate that the improved ACO is effectual.

     

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