Multiple Strategies Particle Swarm Optimization Based Intelligent Route Planning Algorithm

On the premise of ensuring route safety, an intelligent route planning algorithm based on multi-strategy particle swarm optimization is proposed with the lowest energy consumption as the goal. The energy consumption model is established based on airline meteorological information, and a multi-objective fitness function is constructed with energy consumption, airline smoothness and collision penalty as optimization objectives. An adaptive mean velocity updating strategy is proposed, and nonlinear inertia weight is introduced, and Tent chaotic mapping is adopted to dynamically adjust the development ability and exploration ability of the algorithm to avoid falling into local optimal solution. The strategy of updating the position of the worst particle is put forward, and the particles with the worst fitness are updated by coordinate cross, which improves the overall particle quality of the population and speeds up the efficiency of population optimization. The simulation results show that the improved multi-strategy particle swarm optimization algorithm is superior to PSO algorithm in optimization ability and convergence speed.