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

Duan, Haibin; Luo, Qiwu; Shi, Yuhui; Ma, Guanjun

doi:10.1109/mci.2013.2264577

Cited by 220 publications

(97 citation statements)

References 35 publications

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“…Fit individuals are selected to perform the crossover operation to produce a new generation. After that, a mutation operator is applied to the population to decrease the possibility of becoming stuck in a local optimum [25].…”

Section: Genetic Algorithm and Particle Swarm Optimizationmentioning

confidence: 99%

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Zhu

Chang

et al. 2018

Energies

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A radio frequency antenna array was adopted to localize partial discharge (PD) sources in an entire substation. The deployment of an antenna array is a significant factor affecting the localization accuracy, and the array needs to be carefully selected. In this work, a hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) is proposed to optimize the array deployment. A direction-of-arrival (DOA) estimation algorithm applicable to arbitrary array configurations is firstly presented. The Cramér-Rao lower bound (CRLB) was employed to evaluate the localization accuracy of different arrays, and two objective functions characterizing the estimation errors of coordinates and the DOA are proposed. With the goal of minimizing the objective functions, the array deployments for the coordinate and DOA localizations were optimized by using the hybrid PSO/GA algorithm. Using the developed method, optimal antenna configurations for different constraint areas, aspect ratios, and numbers of sensors were investigated. The results indicate that the optimal deployments for coordinate and DOA estimations are different; specifically speaking, superior DOA performance is achieved when all antennas are placed on the outer boundary of the constraint area while part of the antennas in the optimal coordinate array are placed in the middle position.

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Section: Genetic Algorithm and Particle Swarm Optimizationmentioning

confidence: 99%

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Zhu

Chang

et al. 2018

Energies

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“…Its potential benefits include energy savings and greenhouse gas reduction [1,2], improved aircraft coordination within high density airspace [3,4], and mixed operations of Unmanned Aerial Vehicles (UAVs) and manned aircraft [5]. Autonomous formation flight is also the foundation for autonomous aerial refueling [6] and UAV swarm operations [7].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Rice

Huo

et al. 2016

International Journal of Aerospace Engineering

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Autonomous formation flight is a key approach for reducing energy cost and managing traffic in future high density airspace. The use of Unmanned Aerial Vehicles (UAVs) has allowed low-budget and low-risk validation of autonomous formation flight concepts. This paper discusses the implementation and flight testing of nonlinear dynamic inversion (NLDI) controllers for close formation flight (CFF) using two distinct UAV platforms: a set of fixed wing aircraft named "Phastball" and a set of quadrotors named "NEO." Experimental results show that autonomous CFF with approximately 5-wingspan separation is achievable with a pair of low-cost unmanned Phastball research aircraft. Simulations of the quadrotor flight also validate the design of the NLDI controller for the NEO quadrotors.

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“…It allows the UAV to compute the best path from a start point to an end point autonomously [2,3]. Whereas commercial airlines fly constant prescribed trajectories, UAVs in operational areas have to travel constantly changing trajectories that depend on the particular terrain and conditions prevailing at the time of their flight.…”

Section: Introductionmentioning

confidence: 99%

Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning

Zhang

Duan

2014

Lecture Notes in Computer Science

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Abstract. Pigeon-inspired optimization (PIO) is a new bio-inspired optimization algorithm. This algorithm searches for global optimum through two models: map and compass operator model is presented based on magnetic field and sun, while landmark operator model is designed based on landmarks. In this paper, a novel Predator-prey pigeon-inspired optimization (PPPIO) is proposed to solve the three-dimensional path planning problem of unmanned aerial vehicles (UAVs), which is a key aspect of UAV autonomy. To enhance the global convergence of the PIO algorithm, the concept of predator-prey is adopted to improve global best properties and enhance the convergence speed. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO and particle swarm optimization (PSO) in solving UAV three-dimensional path planning problems.

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

Cited by 220 publications

References 35 publications

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Autonomous Close Formation Flight Control with Fixed Wing and Quadrotor Test Beds

Predator-Prey Pigeon-Inspired Optimization for UAV Three-Dimensional Path Planning

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