Evolutionary decision-makings for the dynamic weapon-target assignment problem

Abstract

The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general "virtual" representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.decision-making, dynamic weapon-target assignment (DWTA), military command and control, evolutionary computation, memetic algorithms, constraints handling

Real-time allocation of defensive resources to rockets, artillery, and mortars

“…Another suggested approach is to use the solution returned from the greedy maximum marginal return algorithm and to apply local search on the solution so that the target allocated by one weapon can be swapped to the target allocated by another weapon, and vice versa. In [2], a genetic algorithm combined with local search is suggested for a dynamic version of the asset-based weapon allocation problem. It is shown that local search improves the results compared to use the genetic algorithm without local search, but that the computational time needed is increased.…”

Weapon‐Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition

“…Hongtao and Fengju [23] proposed a new clonal selection algorithm for WTA problem. Chen et al [24] used memetic algorithms for DWTA. Xin et al [25] used the virtual permutation and tabu search heuristics for DWTA.…”

A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems

“…Most research to date on solving WTA problems by heuristic algorithms either constructs some specific search rules based on the properties of the problem to achieve solutions rapidly or introduces some local search mechanisms into the original algorithms to improve the solution quality. These algorithms, including auction algorithms [3][4][5][6]15], improved genetic algorithms [18,[24][25][26], clonal selection algorithms [27,28], particle swarm algorithms [8,13,29], tabu search algorithms [30], rule-based constructive heuristic algorithms [10,31], and other intelligent optimization algorithms, have shown evident advantages over traditional methods in terms of computation time and solution accuracy and however still suffer from some drawbacks, such as easily falling into premature convergence and local optimum [32]. Furthermore, considering the variability of the battlefield environment, decisions always need to be made immediately; that is, WTA problems need to be resolved in a very short time.…”