摘要
面对士兵学历层次,知识理解能力和掌握速度参差不齐的现状,千篇一律的士兵职业技能教育体制已不再适应网络化时代发展和信息化部队建设的需要.文章在分析了当前士兵职业技能教育存在的问题以及蚁群算法和遗传算法各自的特点之后,提出了根据最佳融合点交叉调用蚁群算法和遗传算法的策略,以使蚁群算法的寻优结果作为遗传算法的种子来优化其初始种群,并模仿TSP问题将士兵的个性化学习过程成功地转化为一个典型的组合优化问题,以此来寻找适合每位士兵的个性化学习路径.实验结果表明,改进后的蚁群遗传算法的收敛速度和寻优能力大大提高.
Confronting the situation of uneven educational background, knowledge comprehension and master speed ot soldiers, stereotype education system of soldier＇s occupational skill no longer adapts the demand of network era development and informational military construction. The paper puts forward to the tactic of dynamically call ant algorithm and genetic algorithm according to the best fusion point after analyzing the existing problems in soldier＇s occupational skill education and the features of ant algorithm and genetic algorithm, so as to urge the optimization results of ant algorithm to optimize the initializing population of genetic algorithm. In addition, in order to find the personalized learning path suited to every soldier, the personalized learning process of soldiers is transformed into a typical combinatorial optimization problem successfully by imitating traveling salesman problem. The experiment results show that the convergence rate and optimization capability of the improved ant colony genetic algorithm is greatly improved.
出处
《计算机系统应用》
2015年第11期204-208,共5页
Computer Systems & Applications
关键词
士兵个性化学习
动态蚁群遗传算法
最佳融合点
最优路径
soldier＇s personalized learning
dynamic ant-genetic algorithm
the best fusion point
the optimal path
