ABSTRACT
The chapter discusses a methodology inspired from nature using remote sensing inputs based on swarm intelligence for strategic decision making in modern warfare. The chapter presents a hybrid ant colony–biogeography-based optimization technique (ACO-BBO) for predicting the deployment strategies of enemy troops in the war theatre and finding the shortest and the best feasible path for attack on the enemy base station. The hybrid algorithm begins by predicting the most suitable destination for the enemy troops to position their forces, for which it uses BBO and, after finding the shortest and the best feasible path for attacking the enemy base station, uses the ACO technique, thus combining the strengths of both the techniques. Hence, the algorithm can be used to improve the ACO approach, which is currently used to predict enemy troop mobility since it lacks the ability to predict the destination and can only find a suitable path to the given destination, leading to coordination problems and target misidentification, which can lead to severe casualties. The algorithm can be of major use for the commanders in the battlefield who have been using traditional decision-making techniques of limited accuracy for predicting destination. Using the hybrid ACO-BBO technique can help in enabling commanders for intelligent preparation in the battlefield by automating the process of assessing the likely base stations of the enemy and the ways in which these can be attacked, given the environment and the terrain considerations.
