Multiobjective optimization for the stochastic physical search problem

Jeffrey Hudack, Nathaniel Gemelli, Daniel Brown, Steven Loscalzo, Jae C Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission. We show that our approximate algorithm performs well across a range of uncertainty parameters, with orders of magnitude less execution time than existing solutions on randomly generated instances.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages212-221
Number of pages10
Volume9101
ISBN (Print)9783319190655
DOIs
StatePublished - 2015
Event28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015 - Seoul, Korea, Republic of
Duration: Jun 10 2015Jun 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9101
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015
CountryKorea, Republic of
CitySeoul
Period6/10/156/12/15

Keywords

  • Multiobjective optimization
  • Path planning
  • Planning under uncertainty
  • Stochastic search

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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