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Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs

Phung, Manh Duong and Ha, Quang (2020) Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs. Applied Soft Computing . ISSN 1568-4946 (In Press)

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This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24\% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.

Item Type: Article
Subjects: Information Technology (IT)
Scopus-indexed journals
ISI-indexed journals
Divisions: Advanced Insitute of Engineering and Technology (AVITECH)
Faculty of Electronics and Telecommunications (FET)
Depositing User: Dr Manh Duong Phung
Date Deposited: 13 Oct 2020 08:21
Last Modified: 13 Oct 2020 08:21

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