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Decomposition-Based Memetic Algorithm for Multiobjective Capacitated Arc Routing Problem

机译:多目标电容弧布线问题的基于分解的模因算法

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The capacitated arc routing problem (CARP) is a challenging combinatorial optimization problem with many real-world applications, e.g., salting route optimization and fleet management. There have been many attempts at solving CARP using heuristic and meta-heuristic approaches, including evolutionary algorithms. However, almost all such attempts formulate CARP as a single-objective problem although it usually has more than one objective, especially considering its real-world applications. This paper studies multiobjective CARP (MO-CARP). A new memetic algorithm (MA) called decomposition-based MA with extended neighborhood search (D-MAENS) is proposed. The new algorithm combines the advanced features from both the MAENS approach for single-objective CARP and multiobjective evolutionary optimization. Our experimental studies have shown that such combination outperforms significantly an off-the-shelf multiobjective evolutionary algorithm, namely nondominated sorting genetic algorithm II, and the state-of-the-art multiobjective algorithm for MO-CARP (LMOGA). Our work has also shown that a specifically designed multiobjective algorithm by combining its single-objective version and multiobjective features may lead to competitive multiobjective algorithms for multiobjective combinatorial optimization problems.
机译:电容弧布线问题(CARP)是具有挑战性的组合优化问题,具有许多实际应用,例如盐分路线优化和车队管理。在尝试使用启发式和元启发式方法(包括进化算法)来解决CARP方面已有许多尝试。但是,几乎所有这些尝试都将CARP公式化为一个单一目标的问题,尽管它通常具有多个目标,尤其是考虑到其实际应用。本文研究了多目标CARP(MO-CARP)。提出了一种新的模因算法(MA),称为基于分解的扩展邻域搜索MA(D-MAENS)。新算法结合了用于单目标CARP的MAENS方法和多目标进化优化的先进功能。我们的实验研究表明,这种组合的性能明显优于现成的多目标进化算法,即非支配排序遗传算法II和最先进的MO-CARP(LMOGA)多目标算法。我们的工作还表明,结合其单目标版本和多目标特征进行专门设计的多目标算法可能会导致针对多目标组合优化问题的竞争性多目标算法。

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