Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem  

作　　者：Yu Xue Yi Zhuang Tianquan Ni Siru Ni Xuezhi Wen 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics [2]School of Computer and Software,Nanjing University of Information Science and Technology [3]No.723 Research Institute of China Shipbuilding Industry Corporation

出　　处：《Journal of Systems Engineering and Electronics》2014年第1期59-68,共10页系统工程与电子技术（英文版）

基　　金：supported by the Fundamental Research Funds for the Central Universities(NZ2013306);the Funding of Jiangsu Innovation Program for Graduate Education(CXLX11 0203)

摘　　要：Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.

关 键 词：global optimization SELF-ADAPTIVE discrete differentialevolution weapon-target assignment （WTA） cooperative jamming. 

分 类 号：TN97[电子电信—信号与信息处理]