Task allocation for power grid protection and anti-UAV via iPSO-GA algorithm

In addressing the task allocation problem for an anti-UAV swarm protection system for power transmission lines, a solution based on swarm intelligence optimization algorithms is proposed. While ensuring the accomplishment of anti-drone tasks, an optimization objective is introduced, incorporating energy conservation and loss reduction. Simultaneously considering factors such as strike effectiveness and time constraints, a mathematical model for task allocation optimization is formulated. Subsequently, an improved optimization algorithm, namely the iPSO-GA algorithm, is introduced. By enhancing the Particle Swarm Optimization (PSO) algorithm and integrating it with the Genetic Algorithm (GA), this approach ensures a balance between convergence and diversity in the evolutionary process of the particle swarm. Compared to existing PSO algorithms, it exhibits superior global search capabilities. Simulation experiments demonstrate that this algorithm effectively enhances the task allocation capability of the anti-drone protection system, ensuring the safety of power transmission lines.