Online Parameter Tuned SAHiD Algorithm for Capacitated Arc Routing Problems

Abstract

The Capacitated Arc Routing Problem (CARP) is a general and challenging arc routing problem. As the problem size increasing, exact methods are not applicable, and heuristic and meta-heuristic algorithms are promising approaches to solve it. To obtain good performance, parameter values of heuristics or meta-heuristics should be properly set. In recent years, automatic parameter tuning, which includes off-line and online parameter tuning, has attracted considerable attention in the evolutionary computation community. At present, parameters are usually determined through simple off-line parameter tuning, such as empirical analysis or grid search, when designing algorithms for CARP. However, using off-line parameter tuning on CARP has some disadvantages, among which the computational cost is the serious one. This work proposed an online parameter tuning approach using exponential recency-weighted kernel density estimation (ERW-KDE), and combines it with the SAHiD algorithm, which is an hierarchical decomposition based algorithm for CARP, to constitute the online parameter tuned SAHiD (OPT-SAHiD) algorithm. The experimental results show that OPT-SAHiD significantly outperforms the compared algorithms on two CARP benchmark sets owing to the proposed online automatic parameter tuning approach. The proposed online automatic parameter tuning approach based on ERW-KDE not only improves the performance of SAHiD algorithm, but also removes the additional computational overhead required for offline parameter tuning.