TY  - CONF
SN  - 9781457721502
Y1  - 2011///
EP  - 521
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856759269&doi=10.1109%2fHIS.2011.6122158&partnerID=40&md5=595d6be0e890c115bc9e73a8f63a3fda
A1  - Pierre, D.M.
A1  - Zakaria, N.
A1  - Pal, A.J.
AV  - none
CY  - Malacca
KW  - Algothim; Contrasting Objectives; Genetic; Multi-objective; Path-planning
KW  -  Genetic algorithms; Human engineering; Intelligent systems; Motion planning
KW  -  Unmanned aerial vehicles (UAV)
TI  - Master-slave parallel vector-evaluated genetic algorithm for unmanned aerial vehicle's path planning
ID  - scholars1764
SP  - 517
N2  - 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.
N1  - 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
ER  -