Optimizing Forest Surveillance: A Hybrid Algorithm Combining ACO and ABC

Abstract

This work introduces a potential Hybrid Algorithm for the complex field of forest monitoring, integrating Artificial Bee Colony Optimisation (ABC) and Ant Colony Optimisation (ACO). The performance indicators of the algorithm were carefully assessed in a fictitious use case. Its effectiveness was demonstrated by a shorter drone path, a shorter flight duration, and less energy usage, making it an affordable surveillance option. In addition, the algorithm demonstrated a great rate of mission coverage, quick convergence, and consistently good quality of solutions. Its usefulness in dynamic forest habitats was highlighted by its capacity to adjust to changing weather conditions and scale to accommodate more waypoints. Its cost-effectiveness is increased by efficient resource utilisation, which is demonstrated by low CPU and memory consumption. Taken together, these results highlight how the algorithm may transform forest surveillance by increasing operational effectiveness, cutting expenses, and satisfying the changing requirements of intricate monitoring scenarios. To fully realise the algorithm's potential for environmental monitoring applications, this research advocates for more real-world testing and optimisation.