A Corridor Model Evolutionary Algorithm for Fast Converging Green Vehicle Routing Problem

Ananta Shahane, Niki van Stein, Yingjie Fan

Abstract: In this paper, we introduce an innovative evolutionary algorithm for the Green Vehicle Routing Problem (GVRP). We use the relative location of the customers with respect to the depot to estimate whether a mutation is exploitation or exploration. By introducing a probabilistic corridor model, our mutation strategy converges faster to optimal solutions compared to random mutation strategies in existing research. We then introduce our evolutionary algorithms that use this mutation strategy to reduce the number of hyper-parameters to two or three. We test our algorithms across 92 benchmarks. Results show that our algorithms are robust and efficient.