Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Path planning is the key for unmanned aerial vehicle (UAV) to perform tasks efficiently, which needs quickly obtain optimal path in complex environment. To solve problem urban environment, an improved artificial bee colony algorithm based on multi-strategy synthesis (IABC) proposed generate appropriate UAV. The IABC hybrid mechanism of chaotic mapping and Pareto principle initialize population, so as fully search solution space provide approximate flight Meanwhile, order balance exploration development, two new equations are designed candidate solutions, more superior paths UAVs. In addition, tangent random evolution added enhance strategy updating offspring, maximize quality generation, effectively problem. Aiming at environment multi-constraint optimization UAV flight, combined with cubic spline interpolation a smooth meet maneuver characteristics. this paper has been compared Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Search (SABC) Gbest (GABC). good feasibility effectiveness solving