Application of Quantum Genetic Algorithms to Connected and Electric Vehicles Energy Consumption Optimization

The signal settings design in urban networks requires the solution of complex optimization problems. In particular, the urban networks control when connected and electric vehicles are present has garnered significant i nterest d ue t o its notable advantages over networks with human-driven vehicles. These advantages include the potential for significant reductions in travel time, waiting time, energy consumption, and emissions. In cases where it is not possible to adopt deterministic algorithms to find e xact s olutions, t he l atter c an b e r eplaced w ith suitable approximate solutions provided by evolutionary algorithms. This paper focuses on enhancing these approximation methods by employing a hybrid quantum-classical multiobjective genetic algorithm to optimize the green signal timing for traffic flow regulation between two interconnected junctions. The algorithm, tested on IBM quantum computer simulators, has proven suitable for such optimization task. The experimental results show notable advantages of this technique over traditional evolutionary methods, suggesting its potential for more complex, real-world applications.