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An Improved and Realized Volatility Strategy of the Ant Colony Optimization Algorithm

机译:蚁群优化算法的改进和实现波动策略

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In order to overcome the shortcomings of precocity and stagnation in ant colony optimization algorithm, an improved algorithm is presented. Considering the impact that the distance between cities on volatility coefficient, this study presents an model of adjusting volatility coefficient called Volatility Model based on ant colony optimization(ACO) and Max-Min ant system. There are simulation experiments about TSP cases in TSPLIB, the results show that the improved algorithm effectively overcomes the shortcoming of easily getting an local optimal solution, and the average solutions are superior to ACO and Max-Min ant system.
机译:为了克服蚁群优化算法中的预幂和停滞的缺点,提出了一种改进的算法。考虑到城市之间对波动率系数之间的距离的影响,本研究提出了一种基于蚁群优化(ACO)和MAX-MIN ANT系统的调节挥发性系数的挥发性系数的模型。关于TSPLIB中的TSP案例存在仿真实验,结果表明,改进的算法有效地克服了容易获得局部最佳解决方案的缺点,平均解决方案优于ACO和MAX-MIN ANT系统。

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