Optimization of Weapon–Target Pairings Based on Kill Probabilities

Abstract

In this paper, we present a novel optimization algorithm for assigning weapons to targets based on desired kill probabilities. For the given weapons, targets, and desired kill probabilities, our optimization algorithm assigns weapons to targets that satisfy the desired kill probabilities and minimize the overkill. The minimization of overkill assures that any proper subset of the weapons assigned to a target results in a kill probability that is less than the desired kill probability on such a target. Computational results for up to 120 weapons and 120 targets indicate that the performance of this algorithm yields an average improvement in quality of solutions of 26.8% over the greedy algorithms, whereas execution times remained on the order of milliseconds.

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

“…Lee et al [6] 2002 IS + ACO Static single objective Lee et al [7] 2002 GA Static single objective Lee et al [8] 2003 GA + ACO Static single objective Galati and Simaan [5] 2007 Tabu Dynamic single objective Lee [9] 2010 VLSN Static single objective Xin et al [10] 2010 VP + tabu Dynamic single objective Li and Dong [11] 2010 DPSO + SA Dynamic single objective Chen et al [12] 2010 SA Static single objective Xin et al [13] 2011 Rule-based heuristic Dynamic multiobjective Fei et al [14] 2012 Auction algorithm Static single objective Bogdanowicz et al [15] 2013 GA Static single objective Liu et al [16] 2013 MOPSO Static multiobjective Zhang et al [17] 2014 MOEA/D Static multiobjective Ahner and Parson [18] 2015 Dynamic programming Dynamic multiobjective Li et al [19] 2015 NSGA-II, MOEA/D Static multiobjective Dirik et al [20] 2015 MILP Dynamic multiobjective Hongtao and Fengju [21] 2016 CSA Static single objective Li et al [22] 2016 MDE Dynamic multiobjective Li et al [23] 2017 MPACO Static multiobjective 2 International Journal of Aerospace Engineering population members and also allow the NSGA-III to perform well on MOP with differently scaled objective values. This is an advantage of the NSGA-III and another reason why we choose the NSGA-III algorithm to solve the SMWTA problem.…”

“…where ψ is generated according to an interpolation probability (V itr ) [63] and can be defined by formula (15).…”

A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem

“…Year Metaheuristic algorithm Implementation (WTA) Lee et al [40] 2002 IS + ACO Static single-objective Lee et al [41] 2002 GA Static single-objective Z.-J. Lee and W.-L. Lee [15] 2003 GA + ACO Static single-objective Galati and Simaan [14] 2007 Tabu Dynamic single-objective Lee [13] 2010 VLSN Static single-objective Xin et al [42] 2010 VP + Tabu Dynamic single-objective Li and Dong [16] 2010 DPSO + SA Dynamic single-objective Xin et al [43] 2011 Rule-based heuristic Dynamic multiobjective Chen et al [44] 2010 SA Static single-objective Bogdanowicz et al [10] 2013 GA Static single-objective Fei et al [12] 2012 Auction algorithm Static single-objective Liu et al [18] 2013 MOPSO Static multiobjective Zhang et al [19] 2014 MOEA/D Static multiobjective Ahner and Parson [45] 2015 Dynamic programming Dynamic multiobjective Li et al [20] 2015 NSGA-II, MOEA/D Static multiobjective Dirik et al [46] 2015 MILP Dynamic multiobjective Liang and Kang [47] 2016 CSA Static single-objective Li et al [48] 2016 MDE Dynamic multiobjective decision-making. So, it has aroused wide attention from scholars.…”

“…Hosein and Athans [9] classified the WTA problem into two classes: the single-objective WTA problem and the multiple-objective WTA problem. Genetic algorithm [10], ACO algorithm [11], auction algorithm [12], VLSN algorithm [13], Tabu search [14], and other hybrid algorithms [15][16][17] have been used to optimize single-objective WTA model by many scholars. In contrast to single-objective WTA, multiple-objective optimization can take different criterions into consideration and is more in line with the real combat 2 International Journal of Aerospace Engineering Table 1: Summary of variant metaheuristic algorithms and implementation of various WTA.…”

Decentralized Algorithms for Weapon-Target Assignment in Swarming Combat System

“…WTA plays an increasing role given the growing requirement for the collaborative engagement of several weapons relative to several targets in modern battlefields [1]. Various extant studies focused on solutions for WTA that are mainly applied in the traditional high-value weapon platforms such as air defense WTA [2][3][4] or ship target strikes [5,6].…”