Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

By Vincent Roberge, M. Tarbouchi, G. Labonté. IEEE Transactions on Industrial Informatics, 2013

930 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e1ea6290c1d73fafed5c4c263e6b6f99973cabd7

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, methodology): “So far, real-time optimization is still a challenge to EC algorithms because of the population- and iteration-based characteristics of EC algorithms, despite efforts made on real-time algorithm design [Roberge et al. 2013].”
A survey on vision-based UAV navigation (2018, background): “…vision-based navigation, there are still many problems to be solved before a fully autonomous navigation system coming true, such as autonomously avoiding obstacles, generating an optimal path in dynamic scenarios, and updating and assigning tasks dynamically (Roberge, Tarbouchi, and Labonté 2013).”
Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization (2021, methodology): “GA is implemented as in [25] with three mutation operations: add a node, delete a node and merge two nodes.”
Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System (2015, background): “For the performances of intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear mixed-integer optimization problems, GA has been wildly used [22]–[24].”
MVO-Based 2-D Path Planning Scheme for Providing Quality of Service in UAV Environment (2018, methodology): “, have been used for UAV path planning and trajectory generation [9].”
Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey (2018, background): “Thus, we do not include articles on obstacle-avoiding path planning from a given starting point to a given end point [12, 63, 108, 153, 194, 203, 220, 227, 245, 306] or on planning trajectories of drones flying in formation [220].”
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “The GA is a metaheuristic inspired by the natural selection found in nature [29].”
Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment (2018, methodology): “Although these MOPP algorithms [11]–[15] have been developed to find an achievable path for a UAV flying in a complex environment, none of them has addressed”