基于自适应惯性权重的均值粒子群优化算法

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参数选取等缺点,提出了一种基于自适应惯性权重的均值粒子群优化算法。对算法中的惯性权重参数采用动态自适应变化方式,在迭代过程中根据粒子适应度差值将种群划分为三个等级,对不同等级的粒子采用不同的惯性权重策略,使粒子能根据自己所处的位置选择合适的惯性权重值,更快地收敛到全局最优位置;同时分别用个体极值和全局极值的线性组合取代PSO算法中的全局最优位置与个体最优位置。通过实验仿真与对比,验证了新算法性能优于标准PSO及其它一些改进的PSO算法,能够用较少的迭代次数找到最优解,具有更快的收敛速度和更高的收敛精度。

A mean particle swarm optimization algorithm based on adaptive inertia weight

In order to tackle the problems of slow convergence, low accuracy and parameter dependence of the standard particle swarm optimization (PSO) algorithm, we propose a mean PSO algorithm based on adaptive inertia weight. The new algorithm improves its performance by adjusting inertia weight dynamically and adaptively. It divides particles into three groups according to each fitness value and applies different inertia weight strategies for particles of different groups, making the particles choose appropriate inertia weight values according to their own position, and converges to the global optimal position faster. Furthermore, the individual and global optima are replaced by their linear combination during the algorithm iteration phase to enhance the calculation performance of the PSO. Experimental results show that the proposed algorithm outperforms the standard PSO and some other improved PSO algorithms with less iteration for finding the optimal solution, and has faster convergence and higher convergence precision.