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

Abstract

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