Multi-objective optimization for placing airspace surveillance observers

Detta är en Master-uppsats från Göteborgs universitet/Institutionen för data- och informationsteknik

Sammanfattning: Reconnaissance is an important aspect of military planning. Tools that help an alysts monitor and make informed choices are vital for avoiding costly situations.The use of ground-based radar sensors is a common method for monitoring for bothland-based and airborne threats. Manually finding optimal locations to install sen sors within an area of terrain can be difficult and time intensive, particularly whenmultiple objectives exist. The purpose of this thesis is to implement and comparetwo heuristic algorithms for automatically generating a set of optimal locations forairspace surveillance sensors. The algorithms seek to find solutions that maximizeboth total area coverage and coverage of a specific area of interest. They also seek so lutions that minimize sensor overlap and price. The research problem was formulatedinto a multi-objective optimization. The two algorithms tested include the NSGA-IIand a multi-objective Ant Colony Algorithm (MOACO). A population-halving aug mentation and the Multi-resolution Approach (MRA) developed by Heyns [1] werealso applied to see if algorithm run time could be reduced without impacting finalsolution quality. The NSGA-II outperformed the MOACO algorithm with respect todiversity of the final solution set, however the algorithms performed similarly withrespect to run time and convergence. It was found that population-halving and theMRA could result in computation time reduction for the tested scenario, howevernot at a significant level.

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