改进细菌觅食优化算法的无人机路径规划OA

Aiming at the problems of easily falling into local optimum and low solving efficiency in the complex environment of current UAV path planning,this paper proposes an Improved Bacterial Foraging Optimization(IBFO)algorithm.Firstly,a single-layer loop structure is used to replace the three-layer nested loop structure of the traditional Bacterial Foraging Optimiza-tion(BFO)algorithm,and a multimodal termination rule is designed to greatly improve the convergence speed and stability of the algorithm.Secondly,chaotic variables are introduced in the initialization phase of the BFO algorithm,so that the population can cover more solution space regions and avoid falling into local optimality.Finally,a particle swarm optimization strategy with an enhanced inter-individual communication mechanism and adaptive parameter design is introduced to dynamically adjust the search range and swimming step size of the bacteria according to the environmental concentration at the current location,so as to balance the global and local search capabilities of the algorithm and improve the local search accuracy.The results show that com-pared with the tent multi-strategy improved osprey optimization algorithm(TOOA),the average running time is reduced by 41.4%and the average planning path length is reduced by 19.5%.The experimental results show that the proposed improved bac-terial foraging optimization algorithm has obvious advantages in convergence speed,accuracy and stability,and can effectively deal with complex path planning problems.