Multi-Objective Optimization of Tactical Rescue Strategies Using QHBM and IoT in Urban Conflict Zones

This study addresses the challenge of balancing personnel safety, operational costs, and travel efficiency in complex decision-making environments. This study evaluates the Queen Honey Bee Migration (QHBM) algorithm in comparison to Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Firefly Algorithm (FA) under various risk conditions. The objective of this research is to determine the most effective optimization technique for improving personnel safety, cost efficiency, and convergence rate. This study uses experimental implementation of QHBM, GA, FA, and PSO within specified risk constraints and assesses their performance with optimization metrics. The results show that QHBM outperforms other algorithms in terms of cost efficiency, convergence speed, and personnel safety. QHBM can improve PSO performance by 20% over GA and FA in terms of personnel safety. QHBM is 15% more cost efficient than GA, FA, and PSO methods with the fastest QHBM convergence rate of only 30 to 50 iterations. This study also found the potential of QHBM as a robust and adaptive optimization technique compared to other algorithms. Further research is recommended to implement QHBM in the real world, especially in complex, high-risk work operations.