一种动态调整惯性权重的简化均值粒子群优化算法

Simplified Mean Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight

提出了一种动态调整惯性权重的简化均值粒子群优化算法(DSMPSO).该算法在简化粒子群优化算法的基础上,利用个体最优位置和全局最优位置的线性组合取代算法个体最优位置和全局最优位置,以加快算法的收敛速度以及寻优精度;此外,构造了一种基于余弦函数的惯性权重,并加入服从贝塔分布的随机调整策略,以实现对惯性权重的动态调整,从而更好地平衡算法的全局和局部搜索能力,并增加种群的多样性.仿真实验结果表明,与其他一些改进算法相比,本文的新算法具有更快的收敛速度和更高的寻优精度.

A simplified mean particle swarm optimization algorithm with dynamic adjustment of inertia weight（DSMPSO） is proposed.Based on the simplified particle swarm optimization algorithm,the individual and global optima are replaced by their linear combination so as to accelerate the convergence speed and to improve the precision of the proposed algorithm. Furthermore,the inertia weight is constructed based on Cosine function,the Beta distribution is added into the formula of inertia weight,and thereby the inertia weight can be dynamically adjusted to better balance between the global and local search abilities of the algorithm and to increase the diversity of the particle swarm. The simulation results show that the proposed algorithm has faster convergence speed and higher accuracy than some other improved algorithms.