# A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem

arXiv: https://arxiv.org/abs/2206.03185 · HTML: https://mlsanity.com/paper/2206.03185/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 69 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 4 of 4)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Deep Reinforcement Learning for Electric Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2010.02068/) (2020)
- [A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems](https://mlsanity.com/paper/2010.02369/) (2020)

## Built on by (most cited first, 7 of 7)

- [RL-GA: A Reinforcement Learning-Based Genetic Algorithm for Electromagnetic Detection Satellite Scheduling Problem](https://mlsanity.com/paper/2206.05694/) (2022)
- [Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities](https://mlsanity.com/paper/2308.13420/) (2023)
- [The Emergence of Deep Reinforcement Learning for Path Planning](https://mlsanity.com/paper/2507.15469/) (2025)
- [Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior](https://mlsanity.com/paper/2406.06602/) (2024)
- [Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration](https://mlsanity.com/paper/2506.17357/) (2025)
- [Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2604.13013/) (2026)
- [Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem](https://mlsanity.com/paper/2605.00572/) (2026)

## Abstract (arXiv)

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.