Hybrid particle swarm optimization and genetic algorithm for multi-UAV formation reconfiguration

Haibin Duan, Qinan Luo, Yuhui Shi, Guanjun Ma

Research output: Contribution to journalArticlepeer-review

220 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6557074
Pages (from-to)16-27
Number of pages12
JournalIEEE Computational Intelligence Magazine
Volume8
Issue number3
DOIs
Publication statusPublished - 2013

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