An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem

Abstract

Abstract-In this paper, we propose an efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulation-based DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve large-scale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds.Index Terms-Combinatorial optimization, constraint handling, decision making, dynamic weapon-target assignment (DWTA), heuristic, military operations.

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.…”

“…So, this strategy does not generate biased or special search directions, and then a new direction is selected at random each time. The DE/rand/1 strategy can be defined by formula (13).…”

Modified particle swarm optimization for BMDS interceptor resource planning

“…) is named as "reverse predictor", r 3 is uniformly distributed random numbers bound within the range [0, 1], the inertia weight of the reverse predictor was calculated by (12). Figure 6 shows the process particle moves towards the new position using PSO with reverse predictor.…”

“…Orhan Karasakal presented the issue of allocating air defense missiles to incoming air targets in order to maximize the air defense effectiveness of a naval task group [8]. Rest of the literature proposed different aspects within the WTA problem, interested readers can referred to [9][10][11][12] for a recent survey on WTA problem.…”

Resources dynamic scheduling on the TGs collaborative area air-defense under MCE

“…For a long time, making area air-defense decisions in TG often falls into the problem of weapontarget assignment (WTA) [1], [2], which mainly considers the optimal matching between weapons and targets. Its rationality lies in the fact that the weapon channel in the air defense system cannot be recombined.…”