Multi-UAV Cooperative Monitoring Path Planning by Enhanced Secretary Bird Optimization

With the rapid advancement of UAV technology, its applications in environmental monitoring, infrastructure inspection, and agricultural surveillance have expanded significantly. However, the limited endurance and perception range of a single UAV make it difficult to perform monitoring tasks in large and complex areas. Multi-UAV collaborative inspection has emerged as a research focus, requiring multi-objective optimization under various constraints. To address the challenges of monitoring in restricted flight zones within dynamic environments, a 3D multi-UAV collaborative path planning method based on an Enhanced Secretary Bird Optimization Algorithm (ESBOA) is proposed. A greedy strategy is incorporated during initialization to enhance convergence speed, an elite Levy flight strategy is employed during the predation phase to improve accuracy, and a composite opposition-based learning (COBL) strategy is integrated during the escape phase to boost diversity and stability. Experimental results confirm that ESBOA effectively reduces path planning costs and achieves enhanced optimization performance compared to the original SBOA.