EconPapers    
Economics at your fingertips  
 

Multiobjective Cuckoo Search for Anticipating the Enemy's Movements in the Battleground

Samiksha Goel, Arpita Sharma and V. K. Panchal
Additional contact information
Samiksha Goel: Department of Computer Science, Delhi University, Delhi, India
Arpita Sharma: Department of Computer Science, Delhi University, Delhi, India
V. K. Panchal: Defence and Terrain Research Laboratory, Defence and Research Development Organization, New Delhi, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2014, vol. 5, issue 4, 26-46

Abstract: Since ages nations have been trying to improve their military effectiveness by adopting various measures. Having an anticipatory system, which can not only accurately predict the most probable location for the enemy's base station but also finds the best route to that point, will lead to improved military operations. This paper aims to propose an integrated framework for developing an efficient anticipatory system. In the first phase of the framework, it proposes Anticipatory Multi objective Cuckoo Search (AMOCS) algorithm to identify the best probable location for deployment of enemy forces. For the second phase a hybrid CS-ACO algorithm is developed for obtaining the most suitable path to the location identified in the first phase. To test the proposed system, satellite image of regions of different terrain types namely plain/desert and mountainous respectively, are chosen. Experimental results demonstrate that the system makes accurate predictions.

Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijamc.2014100102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:5:y:2014:i:4:p:26-46

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().

 
Page updated 2025-03-19
Handle: RePEc:igg:jamc00:v:5:y:2014:i:4:p:26-46