群体动画行为自动控制的微粒群优化算法  

Particle swarm optimization algorithm for automatic control of group animation behavior

作　　者：杨亮 张颖 姚奕洋 YANG Liang;ZHANG Ying;YAO Yiyang(Handan University,Handan 056005,China)

机构地区：[1]邯郸学院,河北邯郸056005

出　　处：《现代电子技术》2020年第19期106-110,共5页Modern Electronics Technique

摘　　要：为提升群体动画行为的自主性以及智能性,研究群体动画行为自动控制的微粒群优化算法。用户输入群体动画行为构建目标后,依据固定行为规则规划个体路径,采用微粒群优化算法依据自上而下的控制过程为群体动画内个体选择最优动作。其中,微粒群算法步骤如下:初始化微粒群算法参数、评价微粒初始适应值、更新微粒群内个体速度和位置、评价微粒适应值、判定历史最优位置以及适应值,适应值误差符合设定限制时输出全局最优搜索结果,并选取Eberhart方法依据外部环境变化优化微粒群算法,利用最优动作实现行为自动控制后通过3ds max软件渲染功能将最终结果输出。通过实例分析验证该算法可有效实现群体动画行为的自动控制,且收敛速度快,具有较高的自主性。In order to improve the autonomy and intelligence of the group animation behavior,the particle swarm optimization algorithm for the automatic control of group animation behavior is studied.After a user inputs the group animation behavior to construct the goal,the individual path is planned according to the fixed routine behavior rules,and the particle swarm optimization algorithm is used to select the optimal action for the individual in the group animation according to the top-down control process.The steps of particle swarm algorithm are as follows:initialize the parameters of particle swarm algorithm,evaluate the initial particle adaptive value,update the speed and position of individuals in the particle swarm,evaluate the particle adaptation value,determine the historical optimal position and adaptive value,output the global optimal search results when the adaptive value error meets the set limit,select the Eberhart method to optimize the particle swarm algorithm according to changes of external environment,use the optimal action to achieve automatic control of behavior,and output the final result by the rendering function of 3ds max software.It is verified by example analysis that the algorithm can effectively realize automatic control of group animation behavior,and has fast convergence speed and high autonomy.

关 键 词：群体动画行为 自动控制 微粒群算法优化 动作选择 行为控制 实例分析 

分 类 号：TN911.1-34[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]