摘要
针对微粒群算法(PSO)在搜索过程中粒子的多样性差,易陷入局部最优且收敛速度慢等缺陷,将生物免疫系统中克隆选择机制和独特型免疫网络理论引入到微粒群优化算法中,提出了一种基于免疫机制的PSO优化算法(PSOIM)并将其用于IIR数字滤波器的设计.该算法结合了微粒群算法的全局寻优能力和免疫多样性保持机制,改善了微粒群算法摆脱局部极值点的能力,提高了算法的收敛速度.仿真结果表明该算法在多模态搜索空间中具有更好的全局收敛性能和稳定性,是一种有效可行的IIR数字滤波器设计方法.
Particle swarm optimization has poor diversity, slow convergence speed and is easy to trap into local optimum during the course of searching. Clonal selection mechanism and idiotypic immune network theory exhibited in biological immune system are introduced into particle swarm optimization algorithm, and the particle swarm optimization algorithm based on immune mechanism is proposed and is applied to the design of IIR digital filter. The proposed algorithms have both the properties of the original particle swarm optimization algorithm and the immune diversity keeping mechanism, and can improve the abilities of seeking the global optimum and evolution speed. The simulation results show that the proposed approach has preferable global convergent ability and stability in multi - modal search space, and is a feasible and high efficiency design method for IIR digital filter design.
出处
《微电子学与计算机》
CSCD
北大核心
2009年第9期29-32,共4页
Microelectronics & Computer
基金
中国博士后科学基金项目
江苏省博士后基金项目(0901076C)
关键词
微粒群算法
免疫机制
多样性
早熟收敛
IIR数字滤波器
particle swarm optimization
immune mechanism
diversity
premature convergence
IIR digital filter
