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Hybrid Ant Colony Algorithm for Logistics Distribution Problem with Time Windows

机译:时间窗口物流分布问题的混合蚁群算法

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To solve VRPTW (the Vehicle Routing Problem with Time Windows), the Genetic with Ant Colony Algorithm were mixed as a new algorithm (ACO-GAF). In ant colony state transition probability formula, capacity and time window tightness factors were increased in it; In order to jump out of local optimal, the fish operators were introduced after the crossover and mutation operation of Genetic Algorithm; Then merged optimization solution group; After calculate the fitness function by roulette wheel selection out of the best individual, after the completion of the optimal path pheromone update.On the MATLAB platform, the use of Solomon RC series numerical example in the database, set appropriate parameter values, to the shortest path and the number of vehicles at least as the goal, and ACO-GAF algorithm to solve the numerical example results compared with the current optimal solution, the results show that the ACO-GAF algorithm made a great progress in reducing vehicle; In addition, comparing the results of ACO-GAF algorithm with Genetic Algorithm, Ant Colony Algorithm, the Fish Algorithm, The ACO-GAF algorithm in the optimization efficiency and optimization results are superior to the single algorithm.
机译:为了解决VRPTW(随时间窗口的车辆路由问题),将蚁群算法的遗传混合为新的算法(ACO-GAF)。在蚁群状态过渡概率公式中,其中容量和时间窗紧度因子增加;为了跳出当地最佳,在遗传算法的交叉和突变操作之后引入了鱼类算子;然后合并优化解决方案组;在计算Fourpette轮子选择的健身功能之后,完成最佳路径信息素更新。Matlab平台,在数据库中使用所罗门RC系列数值示例,将适当的参数值设置为最短至少作为目标的路径和车辆数量,以及ACO-GAF算法解决数字示例结果与当前的最佳解决方案相比,结果表明,ACO-GAF算法在还原车辆方面取得了巨大进展;此外,将ACO-GAF算法的结果与遗传算法,蚁群算法,鱼算法,优化效率和优化结果中的ACO-GAF算法进行了比较,优化算法优于单算法。

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