Pareto-optimality for lethality and collateral risk in the airstrike multi-objective problem

Abstract

The recent surge in attacks on terrorist organizations within heavily populated areas has brought precision airstrikes to the forefront of discussion topics among partnering nations. In this paper, we present a quick algorithm for accurately creating the Pareto-optimal frontier in the multi-objective airstrike problem. This algorithm, which leverages off specific attributes of lethality and collateral risk, is shown to routinely outperform differential evolution and enumeration algorithms. Once Pareto optimal solutions are found, they can quickly be converted to solutions for the associated goal-programming and weighted sum scalarization problems. The choice of damage function greatly affects the expected lethality and collateral risk in an airstrike underscoring the need for accurate estimation of weapons effects. Notably, the cookie-cutter damage function underestimates collateral risk while overstating lethality in comparison to other damage functions. In addition, we demonstrate that differing guidelines or damage functions significantly alters the optimal location of the optimal aim point in this targeting problem. The methodology presented greatly improves upon existing work in this field, thus ensuring effective precision airstrikes while maximizing civilian safety.