A GA-ACO hybrid algorithm for the multi-UAV mission planning problem

  • Ke Shang
  • , Stephen Karungaru
  • , Zuren Feng
  • , Liangjun Ke
  • , Kenji Terada

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

24 Scopus citations

Abstract

Multi-UAV mission planning is a combinational optimization problem, that aims at planning a set of paths for UAVs to visit targets in order to collect the maximum surveillance benefits, while satisfying some constraints. In this paper, a genetic algorithm and ant colony optimization hybrid algorithm is proposed to solve the multi-UAV mission planning. The basic idea of the proposed hybrid algorithm is replacing the bad individuals of the GA's population by new individuals constructed by ant colony algorithm. Also, an efficient recombination operator called path relinking is used for mating. A population partition strategy is adopted for improving the evolving efficiency. Experimental results suggested that the proposed hybrid algorithm can solve the test instances effectively in a reasonable time. The comparison study with several existing algorithms shows that the proposed algorithm is competitive and promising.

Original languageEnglish
Title of host publication14th International Symposium on Communications and Information Technologies, ISCIT 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-248
Number of pages6
ISBN (Electronic)9781479944163
DOIs
StatePublished - 15 Jan 2015
Event14th International Symposium on Communications and Information Technologies, ISCIT 2014 - Incheon, Korea, Republic of
Duration: 24 Sep 201426 Sep 2014

Publication series

Name14th International Symposium on Communications and Information Technologies, ISCIT 2014

Conference

Conference14th International Symposium on Communications and Information Technologies, ISCIT 2014
Country/TerritoryKorea, Republic of
CityIncheon
Period24/09/1426/09/14

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