基于改进蚁群算法的运输调度规划

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(Ant Colony Algorithm)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。

Vehicle Routing and Scheduling Problems Based on Improved ACA

Basic ant colony algorithm(ACA) has many shortages when it is used for searching the best route of combinatorial optimization problems including VRSP,such as long searching time,slow convergence rate and easily limited to local optimal solution,etc.The improved ant colony algorithm,which integrates the ant colony algorithm with the genetic algorithm,was proposed to overcome these shortcomings and improve its performance.In each iteration of the ant colony algorithm,adaptive evaporating coefficient was selected to control the convergence rate at first,and then the exact solution was determined by the operations of mutation.At last by demonstrating the power of this approach on a test case,the results derived from the genetic algorithm,basic ACA and the improved ACA were compared and analyzed in the experiment.It proved that the improved ant colony algorithm is effective.