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中文摘要：
针对现有微粒群算法在动态优化问题中容易陷入局部极值问题,提出了一种改进的动态微粒群算法——AVPSO。AVPSO用所有微粒局部最优值的平均值来代替全局最优值,通过有目的的重新初始化部分微粒扩大种群搜索范围,在感知到环境发生变化时迅速、准确地实现对目标的跟踪。实验结果表明,在求解动态优化问题时,AVPSO表现出很好的性能。将AVPSO应用于群体动画中,实现了群体路径规划的自动化。
英文摘要：
To overcome the drawbacks of particle swarm optimizer in dynamic environment, an improved dynamic particle swarm optimizer （AVPSO） is proposed, in which the optimal value is replaced by the average of all local optimal values. By initializing some special particles to keep swarm＇ s diversity, the optimal can be tracked promptly and accurately when the change of the environment is detected. Experimental results demonstrate the AVPSO＇ s good performance when it is used to the dynamic optimization problem. Finally, the AVPSO is applied to path planning of group animation.
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