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Balancing safety and speed in the military path finding problem: analysis of different ACO algorithms

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Juan TorrecillasAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

Pages 2859 - 2864

https://doi.org/10.1145/1274000.1274032

Published: 07 July 2007 Publication History

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Abstract

hCHAC, a MOACO implemented to solve the problem of finding the path that minimizes resources, while maximizing safety for a military unit in realistic battlefields, is compared with some other approaches: two extreme methods, which only considers one objective in the search, and a mono-objective algorithm, which combines the two objectives terms of the formulae in a single. In addition, two state transition rules (combined and dominance-based) have been used in some of the approaches.All of them have been tested in different difficulty maps and hCHAC using the combined state transition rule has been considered the best approach.

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Cited By

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Ahn JJung SKim HHwang HJun H(2023)A study on unmanned combat vehicle path planning for collision avoidance with enemy forces in dynamic situationsJournal of Computational Design and Engineering10.1093/jcde/qwad09910:6(2251-2270)Online publication date: 9-Nov-2023
https://doi.org/10.1093/jcde/qwad099
Leenen LTerlunen A(2013)A focussed dynamic path finding algorithm with constraints2013 International Conference on Adaptive Science and Technology10.1109/ICASTech.2013.6707501(1-8)Online publication date: Nov-2013
https://doi.org/10.1109/ICASTech.2013.6707501
Leenen LTerlunen Ale Roux HRaol JGopal A(2012)A Constraint Programming Solution for the Military Unit Path Finding ProblemMobile Intelligent Autonomous Systems10.1201/b12690-17(225-240)Online publication date: 3-Aug-2012
https://doi.org/10.1201/b12690-17
Show More Cited By

Index Terms

Balancing safety and speed in the military path finding problem: analysis of different ACO algorithms
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
2. Information systems
  1. Information systems applications

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Published In

GECCO '07: Proceedings of the 9th annual conference companion on Genetic and evolutionary computation

July 2007

1450 pages

ISBN:9781595936981

DOI:10.1145/1274000

Program Chairs:
Peter A. N. Bosman
Centre for Mathematics & Computer Science, The Netherlands
,
Tina Yu
Memorial University of Newfoundland, Canada
,
Anikó Ekárt
Aston University, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2007

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, United Kingdom

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Ahn JJung SKim HHwang HJun H(2023)A study on unmanned combat vehicle path planning for collision avoidance with enemy forces in dynamic situationsJournal of Computational Design and Engineering10.1093/jcde/qwad09910:6(2251-2270)Online publication date: 9-Nov-2023
https://doi.org/10.1093/jcde/qwad099
Leenen LTerlunen A(2013)A focussed dynamic path finding algorithm with constraints2013 International Conference on Adaptive Science and Technology10.1109/ICASTech.2013.6707501(1-8)Online publication date: Nov-2013
https://doi.org/10.1109/ICASTech.2013.6707501
Leenen LTerlunen Ale Roux HRaol JGopal A(2012)A Constraint Programming Solution for the Military Unit Path Finding ProblemMobile Intelligent Autonomous Systems10.1201/b12690-17(225-240)Online publication date: 3-Aug-2012
https://doi.org/10.1201/b12690-17
Leenen LVorster Jle Roux HMcGraw RImsand EChinni M(2010)A constraint-based solver for the military unit path finding problemProceedings of the 2010 Spring Simulation Multiconference10.1145/1878537.1878564(1-8)Online publication date: 11-Apr-2010
https://dl.acm.org/doi/10.1145/1878537.1878564
Jakimovski BMeyer BMaehle E(2008)Swarm intelligence for self-reconfiguring walking robot2008 IEEE Swarm Intelligence Symposium10.1109/SIS.2008.4668286(1-8)Online publication date: Sep-2008
https://doi.org/10.1109/SIS.2008.4668286

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hCHAC: A family of MOACO algorithms for the resolution of the bi-criteria military unit pathfinding problem

An ant colony optimization algorithm for the bi-objective shortest path problem

A Successive Approach to Compute the Bounded Pareto Front of Practical Multiobjective Optimization Problems

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