中文摘要：

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.

英文摘要：

In order to improve weapon assignment （WA） accuracy in real scenario, an artificial neural network （ANN） model is built to calculate real-time weapon kill probabilities. Considering the WA characteristic, each input representing one assessment index should be normalized properly. Therefore, the modified WA model is oriented from constant value to dynamic computation. Then an improved invasive weed optimization algorithm is applied to solve the WA problem. During search process, local search is used to improve the initial population, and seed reproduction is redefined to guarantee the mutation from multipoint to single point. In addition, the idea of vaccination and immune selection in biology is added into optimization process. Finally, simulation results verify the modelts rationality and effectiveness of the proposed algorithm.