【摘要】 针对粒子群算法整体上容易陷入局部最优的缺陷,将鱼群算法中的视距、拥挤度引入标准粒子群算法,提出一种改进的粒子群算法,有效提高了粒子群算法的全局收敛性。通过基准函数Sphere、Griewank、Ackley和Shekel’s Foxholes的仿真,验证了改进算法的全局收敛能力。最后,以福建地形为背景,应用改进的粒子群算法完成雷达组网优化部署,进一步验证了改进粒子群算法的有效性。仿真和应用的结果表明,改进后的粒子群算法对于多峰值函数的寻优性能有明显提高。更多还原

【Abstract】 To overcome the drawbacks of sub-optimization involved in standard particle swarm optimization,proposed the improved particle swarm optimization ( FPSO) algorithm by importing visual distance and congestion degree factor of artificial fish-swarm algorithm. The new algorithm enhanced the global search ability of the PSO validated by the four benchmark functions,Sphere,Griewank,Ackley and Shekel’s Foxholes. Considering the Fujian province terrain,used the FPSO to optimize the deployment of the radar network. The completion of the deployment optimization further validated the effectiveness of the FPSO. The results of the experiment indicate that the FPSO improves the optimization performance significantly for multi-peak function.更多还原