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基于改进蚁群算法的运输调度规划

, PP. 137-140

Keywords: 交通工程,运输调度规划,蚁群算法,车辆

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Abstract:

在运输调度等组合优化问题的最优路线的搜索中,传统蚁群算法ACA(AntColonyAlgorithm)存在搜索时间长、收敛速度慢、易陷于局部最优解等缺点.为了克服这些缺点提出了一种改进的蚁群算法,该算法将遗传算法和蚁群算法结合起来,在蚁群算法的每一次迭代过程中,首先采用自适应策略控制它的收敛速度,然后使用变异操作来确定解值,从而提高它的搜索性能.再结合建立的运输调度性能指标,利用遗传算法、蚁群算法和改进蚁群算法3种方法分别进行运输规划,通过比较其时间花费和运输费用,验证了改进蚁群算法的有效性.实践证明,改进后的蚁群算法基本上克服了传统算法自身的不足,提高了算法性能.

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