多目标优化算法在团体操队形变换中的应用

Application of multi-objective optimization algorithm in group calisthenics transformation

针对利用彩排方式编排团体操,成本高且可控性差的问题,提出一种基于元胞自动机的改进多目标粒子群算法,并将其应用于团体操的编排。该算法以元胞空间为单位进行环境建模,引入群体自适应机制,通过粒子适应度和环境标记信息寻找目标点,同时加入归档机制,实现团体操队形编排。仿真实验结果表明,该算法提高了团体操队形变换过程的寻优性、收敛性以及与环境或其他个体的交互性。

Aiming at the problem that rehearsal of group calisthenics costed much and had poor controllability,an improved multiobjective particle swarm optimization algorithm based on cellular automaton was proposed and used in the rehearsal of group calisthenics. This algorithm uses cellular to implement environment modeling,absorbed group adaptive mechanism,looked for target point through the fitness of particle and environment tag information,meanwhile joined the archive mechanism. Simulation experiment indicates that this method overcomes the defect of convergence and improves the intercommunication between individual and environment or individual each other.