| Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms |
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| 引用本文： | 王金柱,刘藻珍,刘敏. Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms[J]. 北京理工大学学报(英文版), 2005, 14(3): 297-301 |
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| 作者姓名： | 王金柱 刘藻珍 刘敏 |
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| 作者单位： | [1]School of Mechatronics Engineering, Beijing Institute of Technology, Beijing 100081, China |
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| 摘 要： | Many factors influencing range of extended range guided munition （ERGM） are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm （GA） is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.
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| 关 键 词： | 遗传算法 参数优化 处罚函数 超射程控制 数字模拟 |
| 文章编号： | 1004-0579（2005）03-0297-05 |
| 收稿时间： | 2004-01-08 |
Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms |
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| WANG Jin-zhu,LIU Zao-zhen and LIU Min. Extended Range Guided Munition Parameter Optimization Based on Genetic Algorithms[J]. Journal of Beijing Institute of Technology, 2005, 14(3): 297-301 |
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| Authors: | WANG Jin-zhu LIU Zao-zhen LIU Min |
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| Affiliation: | School of Mechatronics Engineering, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | Many factors influencing range of extended range guided munition (ERGM) are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm (GA) is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem. |
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| Keywords: | genetic algorithm(GA) parameter optimization penalty function |
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