Testing Environment for Validating Swarm Algorithms in Randomized Settings

Inspired by the collective behavior of organisms such as bee swarms and bird flocks, swarm algorithms are employed in artificial intelligence to tackle complex problems through the collaboration of multiple agents. This paper presents a digital validation environment designed to assess the performance of swarm algorithms. Two prominent algorithms were examined: Particle Swarm Optimization (PSO) and Directional Pheromone Walk (DPW). Given its effectiveness in territory exploration, DPW was selected for this study. Implementation was carried out using the open-source Crazyflie drone. The digital validation environment allows for the simulation and exploration of randomized terrains, facilitating the appropriate selection of swarm algorithms. The proposed integration with hardware enabled rapid and efficient system implementation, demonstrating a robust approach to algorithm validation in virtual environments. This setup allows users to optimize battery usage, costs, and the number of drones in advance for practical system deployment.