摘要
针对导弹滑翔段弹道优化问题,考虑人工鱼群算法局部搜索不精确、微粒群优化算法易发生过早收敛等问题,提出一种新的人工鱼群与粒子群混合优化算法。算法的主要策略是在人工鱼群算法的基础上,将人工鱼群优化算法中的觅食行为变为粒子群在感知范围内进行小范围寻优,在人工鱼群算法的最后,再利用粒子群进行精确寻优。以导弹飞行中的吸热量为优化目标,运用此算法设计得出了导弹滑翔段的优化弹道。
Aiming at the problem of ballistic optimization of glide stage of missile, considering the artificial fish swarm algorithm （AFSA） has a stronger robustness, and has a imprecision of solution and the particle swarm opti- mization algorithm （PSO） is simple and sffective, and is easy in premature convergence, a new hybrid evolutionary algorithm with artificial fish swarm algorithm and particle swarm opimization is put forward. Basing at the AFSA, the main idea of the new algorithm is to translate foraging to optimal in small range in perception range with PSO. At the end of AFSA, PSO is used to optimal again. Optimal ballistic of glide stage is gotten as the optimal aim is heat in the process of missile flight.
出处
《科学技术与工程》
北大核心
2012年第31期8438-8442,共5页
Science Technology and Engineering
基金
装备技术基础项目(EP114054)资助
关键词
小范围寻优
混合算法
滑翔段
精确寻优
弹道优化
small range optimal hybrid algorithm glide stage accurate optimal ballistic optimi- zation
