eprintid: 1764
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/17/64
datestamp: 2023-11-09 15:49:55
lastmod: 2023-11-09 15:49:55
status_changed: 2023-11-09 15:41:18
type: conference_item
metadata_visibility: show
creators_name: Pierre, D.M.
creators_name: Zakaria, N.
creators_name: Pal, A.J.
title: Master-slave parallel vector-evaluated genetic algorithm for unmanned aerial vehicle's path planning
ispublished: pub
keywords: Algothim; Contrasting Objectives; Genetic; Multi-objective; Path-planning, Genetic algorithms; Human engineering; Intelligent systems; Motion planning, Unmanned aerial vehicles (UAV)
note: cited By 10; Conference of 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011 ; Conference Date: 5 December 2011 Through 8 December 2011; Conference Code:88378
abstract: The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties' rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning for UAVs, mainly based on Genetic Algorithm (GA), have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV. © 2011 IEEE.
date: 2011
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856759269&doi=10.1109%2fHIS.2011.6122158&partnerID=40&md5=595d6be0e890c115bc9e73a8f63a3fda
id_number: 10.1109/HIS.2011.6122158
full_text_status: none
publication: Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems, HIS 2011
place_of_pub: Malacca
pagerange: 517-521
refereed: TRUE
isbn: 9781457721502
citation:   Pierre, D.M. and Zakaria, N. and Pal, A.J.  (2011) Master-slave parallel vector-evaluated genetic algorithm for unmanned aerial vehicle's path planning.  In: UNSPECIFIED.