A multi-objective optimal approach for UAV routing in reconnaissance mission with stochastic observation time

Xingguang Peng, Xiaoguang Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 17th International Symposium, ISMIS 2008, Proceedings
Pages246-255
Number of pages10
DOIs
StatePublished - 2008
Event17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008 - Toronto, Canada
Duration: 20 May 200823 May 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4994 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Symposium on Methodologies for Intelligent Systems, ISMIS 2008
Country/TerritoryCanada
CityToronto
Period20/05/0823/05/08

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