Towards an Optimal Hybrid Algorithm for EV Charging Stations Placement using Quantum Annealing and Genetic Algorithms

TL;DR

This paper introduces a hybrid heuristic combining Quantum Annealing and Genetic Algorithms to optimize the placement of EV charging stations, significantly improving solution quality over quantum annealing alone.

Contribution

The paper presents a novel hybrid approach that seeds genetic algorithms with quantum annealing results for better EV charger placement optimization.

Findings

Hybrid method reduces minimum distance from POI by 42.89%.

Hybrid approach outperforms vanilla quantum annealing.

Applicable to general grid-based entity placement problems.

Abstract

Quantum Annealing is a heuristic for solving optimization problems that have seen a recent surge in usage owing to the success of D-Wave Systems. This paper aims to find a good heuristic for solving the Electric Vehicle Charger Placement (EVCP) problem, a problem that stands to be very important given the costs of setting up an electric vehicle (EV) charger and the expected surge in electric vehicles across the world. The same problem statement can also be generalized to the optimal placement of any entity in a grid and can be explored for further uses. Finally, the authors introduce a novel heuristic combining Quantum Annealing and Genetic Algorithms to solve the problem. The proposed hybrid approach entails seeding the genetic algorithms with the results of quantum annealing. Experimental results show that this method decreases the minimum distance from Points of Interest (POI) by $42.89\%$ compared to vanilla quantum annealing over the sample EVCP datasets.