基于蝴蝶优化改进算法的无人机三维路径规划OA

Aiming at the problem of high complexity of path planning and difficulty in generating high-quality paths in effective time for UAVs in complex threat environments,a multi-strategy fusion particle swarm-butterfly optimiza-tion improvement algorithm(IPSOBOA)was proposed.The initial population was optimized through tent chaotic mapping combined with inverse learning strategy to enhance the diversity of the population;nonlinear parameter ad-justment and dynamic conversion probability mechanism were introduced to balance the global search and local ex-ploitation;combined with the particle swarm algorithm,the velocity term was introduced in the local search phase,and the position update equation with dynamic change of velocity was proposed to improve the search efficiency.Based on four benchmark test functions and three different threat scenarios respectively,IPSOBOA was compared with the butterfly optimization algorithm and various other optimization algorithms.The experimental results showed that,compared with the butterfly optimization algorithm in three scenarios of the static environment,IPSOBOA opti-mized the optimal fitness value by 1.8%,17%,and 44%respectively,and optimized the path length by 1.8%,42.4%,and 61.3%respectively;in the dynamic environment,it combined global path tracking and real-time ob-stacle avoidance to generate smoother and safer paths.