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

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 2131–2134

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.

Authors 3

1. Ananta Shahane Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands

2. Bas van Stein Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands

3. Yingjie Fan Aachen

   Leiden University

   Affiliation as printed

   LIACS, Leiden University, Leiden, Netherlands