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

作     者：CHEN Jie1,2, XIN Bin1,2, PENG ZhiHong1,2, DOU LiHua1,2 & ZHANG Juan1,2 1 School of Automation, Beijing Institute of Technology, Beijing 100081, China 2 Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing 100081, China 

作者机构：School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China 

出 版 物：《Science in China(Series F)》 (中国科学（F辑英文版）)

年 卷 期：2009年第52卷第11期

页      面：2006-2018页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 071102[理学-系统分析与集成] 

基　　金：Supported by the National Natural Science Foundation of China (Grant No. 60374069) the Foundation of the Key Laboratory of Complex Systems and Intelligent Science, Institute of Automation, Chinese Academy of Sciences (Grant No. 20060104) 

主　　题：decision-making dynamic weapon-target assignment （DWTA） military command and control evolutionary computation memetic algorithms constraints handling 

摘      要：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.