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The Capacitated Arc Routing Problem (CARP) is an essential and challenging problem in smart logistics. Parameter tuning is commonly encountered in designing and applying heuristic or meta-heuristic algorithms for CARP. Recently, automatic parameter tuning or hyper-parameter optimization, which focuses on automatically finding an optimal parameter setting of an algorithm for problems at hand, has attracted considerable attention and become popular for addressing parameter tuning problems. This paper studies automatic parameter tuning for advanced algorithms in solving CARP. When designing algorithms for CARP, parameters are usually determined through empirical analysis or following some rules of thumb. This paper uses an automatic parameter tuning approach, that is, Bayesian optimization method, to tune an algorithm called SAHiD, which is a scalable approach based on hierarchical decomposition for large-scale CARP. The experimental results show that the algorithm's performance can be significantly improved with automatic parameter tuning. The tuned SAHiD algorithm obtains better solutions and faster convergence speed than original SAHiD on test CARP instances.
Huang et al. (Sat,) studied this question.