eprintid: 4074
rev_number: 7
eprint_status: archive
userid: 285
dir: disk0/00/00/40/74
datestamp: 2020-10-13 08:21:17
lastmod: 2020-10-13 08:21:17
status_changed: 2020-10-13 08:21:17
type: article
metadata_visibility: no_search
creators_name: Phung, Manh Duong
creators_name: Ha, Quang
creators_id: duongpm@vnu.edu.vn
creators_id: quang.ha@uts.edu.au
title: Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVs
ispublished: inpress
subjects: IT
subjects: Scopus
subjects: isi
divisions: avitech
divisions: fac_fet
abstract: 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.
date: 2020-09-10
date_type: published
publisher: Elsevier
official_url: https://doi.org/10.1016/j.asoc.2020.106705
id_number: 10.1016/j.asoc.2020.106705
full_text_status: none
publication: Applied Soft Computing
refereed: TRUE
issn: 1568-4946
citation:   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)