一种基于改进惯性权重的粒子群优化算法

A Particle Swarm Optimization Algorithm Based on Improved Inertia Weight

文章针对标准粒子群算法在迭代过程中容易陷入局部最优,算法本身对惯性权重的调节不够精确这一问题。为了能够提高算法精确性得到更加准确的目标值,提出了一种非线性递减惯性权重策略来优化粒子群算法,使得粒子前期飞行速度下降不会过快,从而平衡粒子的全局搜索能力和局部搜索能力,避免陷入局部最优。最后选择典型测试函数,把改进后的算法与其他改进算法比较,结果表明改进后的算法更加精确稳定性更高。

The article aims at the problem that the standard particle swarm algorithm is easy to fall into the local optimum in the iterative process,and the algorithm itself is not precise enough to adjust the inertia weight.In order to improve the accuracy of the algorithm and obtain a more accurate target value,a non-linear decreasing inertia weight strategy is proposed to optimize the particle swarm algorithm.so that the particle’s early flight speed will not drop too fast,thereby balancing the global search ability of the particle and the local search.Ability to avoid falling into local optimum.Finally,a typical test function is selected,and the improved algorithm is compared with other improved algorithms.The results show that the improved algorithm is more accurate and stable.