基于惯性权重和学习因子动态调整的粒子群算法

针对传统的粒子群算法易发生早熟收敛、在寻优过程中易陷入局部最优等问题,提出了一种基于惯性权重和学习因子动态调整的粒子群算法,该算法通过改进惯性权重和学习因子参数以优化算法.随着算法的不断迭代,其惯性权重以及学习因子随着迭代次数的增加而动态优化,从而平衡其局部寻优能力与全局搜索能力.实验结果表明,改进后的算法在收敛速度以...

Particle Swarm Optimization Algorithm Based on Dynamic Adjustment ofInertial Weight and Learning Factors

In view of such flaws as the premature convergence of the traditional particle swarm optimization algorithm with its liability to fall into the local optimization during the optimization process, a particle swarm algorithm, which is based on dynamic adjustment of inertia weights and learning factors, has thus been proposed. The proposed algorithm improves the inertial weights and learning factor parameters for the optimization of the traditional algorithm. As the algorithm continues to iterate, its inertial weights and learning factors are dynamically optimized with the increase of iteration times, so as to strike a balance between the local optimization ability and the global search ability.The experimental results show that the improved algorithm is superior to the traditional particle swarm optimization algorithm in convergence speed and convergence accuracy, thus helping to improve the premature convergence problem.