TY - GEN
T1 - A multi-objective optimal approach for UAV routing in reconnaissance mission with stochastic observation time
AU - Peng, Xingguang
AU - Gao, Xiaoguang
PY - 2008
Y1 - 2008
N2 - The multiple Unmanned Aerial Vehicles (UAVs) reconnaissance problem with stochastic observation time (MURSOT) is modeled by modifying the typical vehicle routing problem with stochastic demand (VRPSD). The objective consists in optimizing mission duration, total time and the quantity of UAVs. This multi-objective optimization problem is solved using a steady-state multi-objective evolutionary algorithm MOEA with ε-∈dominance conception. In this paper, we propose a heuristic evolutionary operation (HEO) using Insert-to-Nearest Method (INM). Route Simulation Method (RSM) is presented in details to estimate the expected cost of each route and this method is designed especially for our MURSOT. The developed algorithm is further validated on a series of test problems adapted from Solomon's vehicle routing problems. Experimental results show that the INM is capable of finding better solutions in contrast and its advantage is more remarkable as the size of the problem become larger.
AB - The multiple Unmanned Aerial Vehicles (UAVs) reconnaissance problem with stochastic observation time (MURSOT) is modeled by modifying the typical vehicle routing problem with stochastic demand (VRPSD). The objective consists in optimizing mission duration, total time and the quantity of UAVs. This multi-objective optimization problem is solved using a steady-state multi-objective evolutionary algorithm MOEA with ε-∈dominance conception. In this paper, we propose a heuristic evolutionary operation (HEO) using Insert-to-Nearest Method (INM). Route Simulation Method (RSM) is presented in details to estimate the expected cost of each route and this method is designed especially for our MURSOT. The developed algorithm is further validated on a series of test problems adapted from Solomon's vehicle routing problems. Experimental results show that the INM is capable of finding better solutions in contrast and its advantage is more remarkable as the size of the problem become larger.
UR - http://www.scopus.com/inward/record.url?scp=44649165074&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-68123-6_27
DO - 10.1007/978-3-540-68123-6_27
M3 - 会议稿件
AN - SCOPUS:44649165074
SN - 3540681221
SN - 9783540681229
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 246
EP - 255
BT - Foundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
T2 - 17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
Y2 - 20 May 2008 through 23 May 2008
ER -