Research On Modified Particle Swarm Optimization And Their Application In Route Planning For UAV

As an important component of mission planning, route planning for Unmanned Aerial Vehicles (UAV) in both military and civilian applications is getting more and more attention for researchers and has become a reasearch hopspot in recent years. The particle swarm optimization algorithm and its application in route planning for UAV is studied in this thesis. Considering the characteristics of route planning for UAV, the route planning methods based on particle swarm optimization algorithm and its improved algorithms are investigated deeply in this paper. The main contents of this paper include:(1) the convergence analysis of particle swarm optimization algorithm; (2) route planning for UAV based on quantum-behaved particle swarm optimization (QPSO) with breeding strategy; (3) route planning for UAV based on phased angle-encoed and quantum-behaved particle swarm optimization; (4) route planning for UAV based on hybrid differential evolution with QPSO.The empirical region of algorithm parameters was divided into four small regions, where the convergence and divergence properties of standard particle swarm optimization (PSO) algorithm were studied. At the same time, the relationship between the characteristic roots and algorithm parameters were analyzed. A series of conclusions were deduced through rigorous mathematics derivation. Finally, numerical simulation demonstrated the different effects of different algorithm parameters on the loca of particle position and particle velocity, and further illustrated the validity of conclusions given in this paper. At the same time, the parameters selection strategy for PSO was given, which provided the theoretical basis for the selection of parameters of PSO in the simulation experiments of the following chapters.In view of the premature convergence problem of PSO, a hybrid quantum-behaved particle swarm optimization algorithm (HQPSO) was presented by introducing the breeding strategy into QPSO in this paper. A method of 3-D route planning for UAV was set up base on the proposed HQPSO algorithm. The performance of HQPSO was compared against the QPSO and PSO with inertial weight through using statistical method. Simulation results demonstrate that HQPSO not only has stronger global searching ability, but also achieves a faster convergence speed compared with QPSO and PSO. The path planner based on HQPSO can find better path with faster convergence speed. In addition, the 3-D path generated by HQPSO algorithm can fulfill the threat avoidance and terrain following effectively.Through changing the coding mode, the phase angle-encoded and quantum-behaved particle swarm optimization (0-QPSO) is proposed in this paper. Several representative benchmark functions are selected as testing functions. The real-valued genetic algorithm (GA), differential evolution (DE), PSO, phase angle-encoded particle swarm optimization (θ-PSO), QPSO, andθ-QPSO are tested and compared with each other on the selected unimodal and multimodal functions. To corroborate the results obtained on the benchmark functions, a new route planner for UAV is designed to generate a safe and flyable path in the presence of different threat environments based on theθ-QPSO algorithm. The PSO,θ-PSO, and QPSO are presented and compared with theθ-QPSO algorithm as well as GA and DE through the UAV path planning application. Experimental results demonstrated good performance of theθ-QPSO in planning a safe and flyable path for UAV when compared with the GA, DE, and three other PSO-based algorithms.Finally, this paper presents a hybrid DE with QPSO for the UAV route planning on the sea. A simple method of pretreatment to the terrain environment is proposed to reduce the complexity of problem and improve the computational efficiency. The terrain pretreatment includes extracting the contour of islands and fitting them by ellipses. In consideration of the conflicet between sample accuracy and computation efficiency, a new method based on the location relation of path segement and threat region is proposed. To show the high performance of the proposed method, the DEQPSO algorithm is compared with the GA, DE, PSO, hybrid particle swarm with differential evolution operator (DEPSO), and QPSO, in the presence of different threat environments, which mean different start points, endpoints, and radar threats. Experimental results demonstrated that the proposed method is capable of generating higher quality paths stably and efficiently for UAV than any other tested optimization algorithms.