IEEE Trans Cybern. 2016 Apr;46(4):1000-13. doi: 10.1109/TCYB.2015.2419276. Epub 2015 Apr 22.
The capacitated arc routing problem (CARP) has attracted considerable attention from researchers due to its broad potential for social applications. This paper builds on, and develops beyond, the cooperative coevolutionary algorithm based on route distance grouping (RDG-MAENS), recently proposed by Mei et al. Although Mei's method has proved superior to previous algorithms, we discuss several remaining drawbacks and propose solutions to overcome them. First, although RDG is used in searching for potential better solutions, the solution generated from the decomposed problem at each generation is not the best one, and the best solution found so far is not used for solving the current generation. Second, to determine which sub-population the individual belongs to simply according to the distance can lead to an imbalance in the number of the individuals among different sub-populations and the allocation of resources. Third, the method of Mei et al. was only used to solve single-objective CARP. To overcome the above issues, this paper proposes improving RDG-MAENS by updating the solutions immediately and applying them to solve the current solution through areas shared, and then according to the magnitude of the vector of the route direction, and a fast and simple allocation scheme is proposed to determine which decomposed problem the route belongs to. Finally, we combine the improved algorithm with an improved decomposition-based memetic algorithm to solve the multiobjective large scale CARP (LSCARP). Experimental results suggest that the proposed improved algorithm can achieve better results on both single-objective LSCARP and multiobjective LSCARP.
带容量约束的弧路由问题 (CARP) 因其在社会应用方面的广泛潜力而引起了研究人员的极大关注。本文基于 Mei 等人最近提出的基于路径距离分组 (RDG-MAENS) 的协同进化算法进行构建和拓展。尽管 Mei 的方法已经被证明优于以前的算法,但我们讨论了几个仍然存在的缺点,并提出了解决这些缺点的方法。首先,尽管 RDG 用于搜索潜在的更好的解决方案,但在每个生成的分解问题中生成的解决方案不是最佳的,并且迄今为止找到的最佳解决方案没有用于解决当前生成的问题。其次,仅仅根据距离来确定个体属于哪个子群体可能会导致不同子群体之间个体数量和资源分配的不平衡。第三,Mei 等人的方法仅用于解决单目标 CARP。为了克服上述问题,本文提出了通过立即更新解决方案并通过共享区域将其应用于解决当前解决方案来改进 RDG-MAENS,然后根据路线方向向量的大小,并提出了一种快速而简单的分配方案来确定路线所属的分解问题。最后,我们将改进的算法与改进的基于分解的混合遗传算法相结合,以解决多目标大规模 CARP (LSCARP)。实验结果表明,所提出的改进算法在单目标 LSCARP 和多目标 LSCARP 上都能取得更好的结果。