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

• Published in: Science China. Information sciences 2009-11, Vol.52 (11), p.2006-2018
• Main Authors: Chen, Jie, Xin, Bin, Peng, ZhiHong, Dou, LiHua, Zhang, Juan
• Format: Article
• Language: English
• Subjects: Command and control; Computer Science; Decisions; Evolutionary algorithms; Feasibility; Genetic algorithms; Greedy algorithms; Information Systems and Communication Service; Operations research; Optimization models; Permutations; Representations; Weapons; 决策算法; 分配问题; 制约因素; 局部搜索; 武器目标分配; 遗传算法
• ISSN: 1009-2757; 1674-733X; 1862-2836; 1869-1919
• DOI: 10.1007/s11432-009-0190-x

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.