Application of Improved Pigeon Swarm Algorithm in Distributed Adjustable Resource Scheduling Optimization

Since the traditional scheduling method fails to adequately address the dynamically changing resource requirements and system constraints, a more intelligent and adaptive optimization algorithm is needed to improve the scheduling quality. This study proposes an improved pigeon flock algorithm (Improved Pigeon-Inspired Optimization, IPIO) to balance the ability of local and global search through adaptive adjustment mechanism, and adopts population grouping and random walk strategies to increase population diversity, thus enhancing the optimization ability of the algorithm. In the resource scheduling model, the key technologies that the algorithm needs to handle include task allocation, resource optimization, and scheduling strategies. IPIO estimates the distance between nodes by receiving signal intensities and estimates node locations using improved algorithms, thus enabling efficient scheduling of resources. The simulation experiments evaluate the performance of IPIO in localization accuracy and convergence speed by setting different scenarios and parameters, and compare it with the traditional pigeon flock algorithm. IPIO shows high localization accuracy and fast convergence speed in the scheduling optimization of distributed adjustable resources. Compared with the traditional pigeon flock algorithm, the convergence probability of IPIO reaches a maximum of 94% and a minimum of 88%. Moreover, the stability and reliability of IPIO have also been validated, indicating that it is suitable for resource scheduling scenarios with high requirements for real-time and dynamic adaptability.