Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration

• Published in: IEEE computational intelligence magazine Vol. 8; no. 3; pp. 16 - 27
• Main Authors: Duan, Haibin, Luo, Qinan, Shi, Yuhui, Ma, Guanjun
• Format: Magazine Article
• Language: English
• Published: IEEE 01.08.2013
• Subjects: Approximation methods; Genetic algorithms; Optimal control; Particle swarm optimization; Piecewise linear approximations; Unmanned aerial vehicles
• ISSN: 1556-603X; 1556-6048
• DOI: 10.1109/MCI.2013.2264577

The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way, the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference, the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attracted many researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper, a Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.