Optimal Multi-objective Urban Tactical Position Selection


Genetic algorithms have gained popularity as effective search procedures for obtaining solutions to traditionally difficult problems. In this research-in-progress paper, we present a new method based on a multi-objective genetic algorithm to study the urban tactical defensive position selection problem. This is an important real world application, not only because cities have been viewed as centers of gravity by military planners throughout history, but also because the military significance of cities has increased proportionally as the global urbanization does. We present a mapping between the domain of tactical position selection and that of the multi-objective optimization model. Fronts of Pareto optimal positions are generated and novel force deployment plans are identified for urban defensive missions.