CHAC, A MOACO algorithm for computation of bi-criteria military unit path in the battlefield: Presentation and first results

International Journal of Intelligent Systems

Volumn 24, Issue 7, 2009, Pages 818-843

  Mora, A M ^a   Merelo, J J ^a   Laredo, J L J ^a   Millan, C ^a,b   Torrecillas, J ^a,b  

^a UNIVERSITY OF GRANADA   (Spain)

^b ETS Ingenierías Informática y Telecomunicaciones ^*  (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ANT-COLONY OPTIMIZATION; BI-CRITERIA; GREEDY ALGORITHMS; MILITARY UNITS; MULTI OBJECTIVE; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; RULE BASED; STATE TRANSITION RULE;

COMPUTATION THEORY; HEURISTIC ALGORITHMS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 67650835176     PISSN: 08848173     EISSN: 1098111X     Source Type: Journal    
DOI: 10.1002/int.20362     Document Type: Article

References (22)

1. Stenz A. Optimal and efficient path planning for unknown and dynamic environments. Tech. Rep. CMU-RI-TR-93-20, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA; 1993.
2. Stenz A. Optimal and efficient path planning for partially-known environments. In: Proc ICRA, San Diego, CA: 1994; pp 3310-3317.
3. Dorigo M, Di Caro G. The ant colony optimization metaheuristic. In: Corne D, Dorigo M, Glover F, editors. New ideas in optimization. McGraw-Hill, 1999. pp 11-32.
4. Dorigo M, Stützle T. The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Kochenberger GA, Glover F, editor. Handbook of metaheuristics, Boston, MA: Kluwer; 2002. 251-285.
5. Coello Coello CA, Van Veldhuizen DA, Lamont GB. Evolutionary algorithms for solving multi-objective problems. Boston, MA: Kluwer; 2002.
6. Barán B, Schaerer M. A multiobjective ant colony system for vehicle routing problem with time windows. In: IASTED Int Multi-Conference on Applied Informatics, number 21 in IASTED IMCAI; 2003. pp 97-102. Also available at http://www.scopus.com/scopus/ inward/record.url?eid=2-s2.0- 1442302509&partner=40&rel=R4.5.0.
7. Iredi S, Merkle D, Middendorf M. Bi-criterion optimization with multicolony ant algorithms. In: Zitzler E, Deb K, Thiele L, Coello Coello CA, Corne D, editors. Proc the First Int Conf on Evolutionary Multi-Criterion Optimization (EMO 2001), Lecture Notes in Computer Science, Vol. 1993. Berlin: Springer-Verlag; 2001. pp 359-372.
8. Mariano CE, Morales E. MOAQ: An ant-Q algorithm for multiple objective optimization problems. In: Genetic and Evolutionary Computation Conference (GECCO-99); 1999. 894- 901.
9. Mariano CE, Morales E. A multiple objective Ant-Q algorithm for the design of water distribution irrigation networks. Technical Report HC-9904, Instituto Mexicano de Tecnología del Agua, Mexico; 1999. Available at http://www.lania.mx/∼ccoello/mariano99a.ps.gz
10. López-Ibáñez M, Paquete L, Stützle T. On the design of ACO for the biobjective quadratic assignment problem. In: Dorigo M, Birattari M, Blum C, Gambardella L, Montada F, Stützle T, editors. Proc Fourth Int Workshop on Ant Colony Optimization (ANTS 2004); Lecture Notes in Computer Science, Vol. 3172. Berlin: Springer; 2004. pp 214-265.
11. García-Martínez C, Cordón O, Herrera F. A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur J Oper Res, 2007; 180(1):116-148.
12. Manley K. Pathfinding: from A* to LPA. Available at http://khorshid.ece.ut.ac.ir/∼acm/ ejournal/public/pdf/from-a-star-to-lpa. pdf
13. Yap P, Schaeffer J. Path-finding on a grid. In: Caulfield JH., Chen SH, Cheng HD, Duro R, Caufield JH, Chen SH, Cheng HD, Duro R, Honavar V. editors, In: Proc of the 6th Joint Conf on Information Sciences, JCIS 2002: 2002.
14. Lee Z-J, Lee C-Y, Su S-F. An immunity-based ant colony optimization algorithm for solving weapon-target assignment problem. Appl Soft Comput 2002; 2(1):39-47.
15. Montana D, Bidwell G, Vidaver G, Herrero J. Scheduling and route selection for military land moves using genetic algorithms. In: 1999 Cong on Evolutionary Computation, Washington, DC; July 1999. Piscataway NJ: IEEE Vol. 2, pp 1118-1123.
16. Maloney PS. An application of neural net technology to surveillance information correlation and battle outcome prediction. In: Proc IEEE 1989; 2:948-955. National Aerospace and Electronics Conference, NAECON, 1989.
17. Thyagarajan K, Batta R, Karwan MH, Szczerba RJ. Planning dissimilar paths for military units. Military Oper Res J 2005; 10(1):25-42.
18. Saab Y, VanPutte M. Shortest path planning on topographical maps. IEEE Trans. Systems, Man, Cybern A 1999; 29(1):139-150.
19. Akgün I, Tansel BC. Optimization of transportation requirements in the deployment of military units. Comput Oper Res 2005; 34(4):1158-1176. Also available at http://www. sciencedirect.com/science/article/B6VC5-4H0S77T-2/2/ 15ae9088986c179499071995e633 81c2.
20. Bull S. World war II infantry tactics. Company and battalion. Oxford, UK: Osprey Publishing; 2005.
21. Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans Evol Comput 1999;3(4):257-271.
22. Mora AM, Merelo JJ, Millán C, TorrecillasJ, Laredo JLJ, Castillo PA. Enhancinga MOACO for solving the bi-criteria pathfinding problem for a military unit in a realistic battlefield. In: Giacobini M, editor. EvoWorkshops 2007. Applications of Evolutionary Computing. Lecture Notes in Computer Sciences, Vol. 4448. Berlin: Springer; 2007. pp 712-721.