Summary: Three diﬀerent optimisation algorithms were evaluated: Simulated annealing, Tabu Search and the genetic algorithm NSGA-II. The optimisation algorithms were tested in three diﬀerent scenarios to reduce bias. The evaluation showed that NSGA-II had consistent gains over the other two algorithms.
Master’s Thesis: Automated decision support for placing terrain observers
May 10, 2019
Objective for the Master’s Thesis: Access to reliable information is key for military decision-making. Reconnaissance assets are used to gather information about the Operational Environment. These assets need to be placed within the terrain so that they can see as much of the area of interest as possible. The manual task of placing assets within the terrain is a time-consuming task.
This thesis examines the design for a system that could be used to generate candidate placement positions to aid the decision-maker. The system’s task is to ﬁnd positions that maximise visual cover, while keeping the assets as safe as possible. The problem was formalised and reformulated into a multi-objective optimisation problem.