This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.

It's of vital importance to estimate the unknown parameters of chaos systems in chaos control and synchronization. We firstly improve the newly developed particle swarm optimization (PSO) in view of the population initialization and objective function treatment. Then we use the improved algorithms for parameter estimation and on-line estimation of chaotic system for its global searching ability. Experiments show that the improved method has better adaptability, reliability and high precision is robust to noise. It is proved to be a successful approach in parameter estimation for chaotic systems.