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Improved Memetic Algorithm for Multi-depot Multi-objective Capacitated Arc Routing Problem

机译:改进的多站点多目标电容弧布线问题的模因算法

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The capacitated arc routing problem (CARP) is a challenging vehicle routing problem with numerous real-world applications. In this paper, an extended version of CARP, the multi-depot multi-objective capacitated arc routing problem (MDMOCARP) is proposed to tackle practical requirements. Firstly, the critical edge decision mechanism and the critical edge random allocation mechanism are proposed to optimize edges between depots. Secondly, a novel adaptive probability of local search with fitness is proposed to improve the Decomposition-Based Memetic Algorithm for Multi-Objective CARP (D-MAENS). Compared with the D-MAENS algorithm, experimental results on MD-CARP instances show that the improved memetic algorithm (IMA) has performed significantly better than D-MAENS on convergence and diversity in the metric IGD and the metric HV.
机译:电容弧布线问题(CARP)是具有众多实际应用的具有挑战性的车辆布线问题。本文提出了CARP的扩展版本,即多仓库多目标电容弧布线问题(MDMOCARP),以解决实际需求。首先,提出了临界边缘决策机制和临界边缘随机分配机制,以优化仓库之间的边缘。其次,提出了一种具有适应性的局部搜索自适应概率,以改进基于分解的多目标CARP算法(D-MAENS)。与D-MAENS算法相比,在MD-CARP实例上的实验结果表明,改进的模因算法(IMA)在度量IGD和度量HV的收敛性和多样性方面表现出明显优于D-MAENS。

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