ID	原文	译文
7754	舰炮对岸射击问题需要求取表尺梯次差及射击弹丸数量,区域射击方法能够优化求解这些参数。	Naval gun across the shooting problems need to calculate the markings phase difference and shooting tiny quantity, method of shooting solution to optimize these parameters.
7755	给出舰炮对岸射击毁伤概率模型,引入中间函数通过变分求解最优中间函数,进而提出表尺梯次差求解方法。	Across the naval gun shot kill probability model, introducing the intermediate function by solving the optimal middle of variational function, put forward the markings phase difference method.
7756	根据预定毁伤概率要求,构建迭代模型,给出基于预定毁伤概率的射击弹丸数量求解方法。	Construction iteration model, according to the requirement due to kill probability is given based on the number of shooting the projectile due to kill probability method.
7757	通过数值求解得到典型题设下的表尺梯次差及射击弹丸数量,并与现有方法进行了对比分析。	Typical question set by the markings is obtained by numerical phase difference and shooting number of projectile, and has carried on the comparative analysis with the existing method.
7758	分析结果表明,方法能够给出满足预定毁伤概率要求的舰炮对岸射击方案,与现有方法相比,模型精确性和优化程度更高。	The analysis results show that the method can meet the requirement due to kill probability is given the other side of the naval gun firing solution, compared with existing methods, model accuracy and optimization degree is higher.
7759	同时,在无法确定目标分布特性时,可以假设目标服从均匀分布并进行舰炮对岸射击方案求解,虽然会增加射击弹丸数量消耗,但是可以保证毁伤概率达到预定要求。	At the same time, the distribution characteristics of the target, the target are assumed to be evenly distributed for naval gun firing scheme to solve the other side, while the number will increase shooting the projectile consumption, but it can guarantee the kill probability to the expected requirements.
7760	针对复杂的大规模多背包问题,提出了一种基于高级人工鱼群算法的求解方法。	For complex large-scale knapsack problem, this paper proposes a solving method based on advanced artificial fish algorithm.
7761	为了解决人工鱼群算法收敛速度慢、求解精度低的问题,所提算法通过改进其初始化方法,优化人工鱼个体的行为选择方式和追尾行为来加快问题求解的收敛速度;	In order to solve the slow convergence speed, artificial fish algorithm to solve the problem of low precision, the proposed algorithm by improving its initialization method, optimization of artificial fish to individual behavior choice and train to speed up the convergence speed of problem solving;
7762	同时引入了动态视野及步长和人工鱼调整策略来提高算法搜索的精度。	At the same time introduced the dynamic field of vision and step length and artificial fish to adjust strategies to improve the precision of the algorithm.
7763	仿真实验表明:与现有的算法相比,所提算法不仅能快速收敛,而且可以达到更高的精度,尤其是对于规模越大的多背包问题算法性能提升越明显。	Simulation results show that: compared with the existing algorithms, the proposed algorithm can not only fast convergence, and can reach higher precision, especially for the larger the multiple knapsack problem algorithm performance, the more obvious.