面向可交互式智慧鱼群的权重动态约束的粒子群方法  

Particle Swarm Optimization Method Based on Weighted Dynamic Constraints for Interactive Intelligent Fish Swarm

作　　者：蔡兴泉[1] 布尼泓灏 李梦璇[1] 李凤霞[2] 

机构地区：[1]北方工业大学计算机学院,北京100144 [2]北京理工大学智能信息技术北京市重点实验室,北京100081

出　　处：《系统仿真学报》2016年第10期2490-2496,共7页Journal of System Simulation

基　　金：国家自然科学基金(61503005);北京市自然科学基金(4162022);北方工业大学长城学者(CC08)

摘　　要：针对粒子群算法在短程迭代的状况下搜索精度差、波动大、粒子状态考证不足的问题,提出了一种面向可交互式智慧鱼群的权重动态约束的粒子群算法。根据粒子状态将粒子群进行分离,对粒子群进行动态约束管理,并使用"系数收敛管理器"的概念保留了粒子间的差异化运动。设定估价函数,采用权重动态约束,完成粒子群的快速求解,并使之应用于智慧鱼群模拟。结果表明,在大规模虚拟生物集群移动中,权重动态约束效果最好;完成智慧鱼群运动时,明显优于普通粒子群算法,且速度明显加快。该方法已经很好的用在了自主开发的虚拟水族馆系统中,运行稳定可靠。For particle swarm algorithm not being applied to the rapid evolution of virtual biological cluster in short range, a particle swarm optimization method was provided that oriented to interactive intelligent fish with weight dynamic constrained. This method let particle swarm through the state of the particle separation, and dynamic constraint particle swarm. This method used the concept of ＂convergence coefficient manager＂ to retain the differential movement between the particles. On this basis, setting the evaluation function and using the dynamic constraint weights, the fast particle swarm was completed, applying to virtual biological cluster. The experiments results show the best weights dynamic constraint effect in large-scale virtual biological cluster and intelligent fish pattern movement, and this method is more effect than common particle swarm optimization algorithm, and accelerates significantly. And this method has been used in the development of virtual aquarium system with stable and reliable.

关 键 词：智慧鱼群 权重动态约束 粒子群 估价函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]