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首页> 外文期刊>Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence >Comparison of Recombination Operators in Panmictic and Cellular GAs to Solve a Vehicle Routing Problem
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Comparison of Recombination Operators in Panmictic and Cellular GAs to Solve a Vehicle Routing Problem

机译:解决泛型遗传算法和细胞遗传算法中重组算子的比较

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The Vehicle Routing Problem (VRP) deals with the assignment of a set of transportation orders to a fleet of vehicles and the sequencing of stops for each vehicle to minimize transportation costs. This paper presents two different genetic algorithm models (panmitic and cellular models) for providing solutions for the Capacitated VRP (CVRP), which is mainly characterized by using vehicles of the same capacity. We propose a new problem dependent recombination operator, called Best Route Better Adjustment recombination (BRBAX), which incorporates problem specific knowledge such as information about the routes constitution. A comparison of its performance is carried out with respect to classical recombination operators for permutations. A complete study of the influence of the recombination operators on the genetic search is presented. The results show that the use of our specialized BRBAX operator outperforms the others more generic operators for all problem instances under all metrics. The deviation between our best solution and the best-known one is very low, under 0.91%.
机译:车辆路线问题(VRP)负责将一组运输订单分配给一组车队,并对每个车辆的停靠点进行排序,以最大程度地降低运输成本。本文介绍了两种不同的遗传算法模型(泛能模型和细胞模型),它们为电容式VRP(CVRP)提供解决方案,其主要特点是使用相同容量的车辆。我们提出了一种新的依赖于问题的重组算子,称为最佳路由更好的调整重组(BRBAX),它结合了特定于问题的知识,例如有关路由构成的信息。相对于用于排列的经典重组算子,对其性能进行了比较。提出了完整的重组操作员对遗传搜索影响的研究。结果表明,对于所有指标在所有指标下的使用,我们专用的BRBAX运算符的使用均优于其他更通用的运算符。我们最好的解决方案与最著名的解决方案之间的偏差非常小,不到0.91%。

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