45118 Integration of Quantum Genetic Heuristics to Optimize Unmanned Aerial Vehicle (UAV) Navigational Path
Gurwinder Singh, Preet Kamal · 2025
This chapter presents an integration of quantum genetic heuristics (QGH) to optimize the performance of unmanned aerial vehicles (UAVs) overcoming the limitations of traditional genetic algorithms (GAs) in complex optimization scenarios. Quantum-inspired operators such as superposition and entanglement with adaptive mutation and crossover rates Experimental results show that it uses the nomenclature showing that QGH outperforms GA in several evaluation criteria. In particular, QGH obtained the best combined objective value of 17.53 compared to 19.33 for GA, indicating a better optimization efficiency. The convergence rate of QGH was fast and consistent, and QGH exhibited low robustness in different generations, and demonstrated its robustness and ability to avoid local variations. The rate of variation and different selection methods included helped QGH maintain a high level of solution diversity throughout the development process. This chapter establishes QGH as a viable alternative to conventional GA, especially for applications requiring they are energy efficient, integrate quickly and strongly in dynamic environments.