一种动态调整惯性权重的粒子群优化算法

针对粒子收敛速度慢、搜索精度不高和算法性能在很大程度上依赖于参数的选取等缺点,提出了一种非线性指数惯性权重粒子群优化算法(Exponential Inertia Weight in Particle Swarm Optimization,EIW-PSO)。在每次迭代的过程中, 采用粒子最大适应值和最小适应值的指数函数来动态调整 算法中的惯性权重,更有利于算法在寻优过程中跳出局部最优;同时,引入随机因子以确保种群的多样性,使粒子更快地收敛到全局最优位置。为了验证该算法的寻优性能,通过8个基准测试函数将标准PSO、线性递减惯性权重LDIW-PSO、均值自适应惯性权重MAW-PSO在不同维度和种群规模下进行测试比较。实验结果表明,提出的EIW-PSO算法具有更快的收敛速度和更高的求解精度。

Particle Swarm Optimization Algorithm with Dynamically Adjusting Inertia Weight

In order to tackle the problems of slow convergence,low accuracy and parameter dependence of the standard particle swarm optimization(PSO) algorithm,a nonlinear exponential inertia weight in particle swarm optimization(EIW-PSO) was proposed.In each iteration,the new algorithm improves its performance by adjusting inertia weight dynamically.The new weight is an exponential function of the minimal and maximal fitness of the particles,which is more conducive for the algorithm being out of local optimization in optimization process. Random factors are introduced to ensure population diversity,so that the particles converge to the global optimal position faster.The standard PSO,linearly decreasing inertia weigh (LDIW-PSO),mean adaptive inertia weigh (MAW-PSO) were tested and compared in different dimensions and population sizes through eight benchmark test functions.Experimental results show that the proposed EIW-PSO algorithm has faster convergence rate and higher solving precision.