动态调整惯性权重的粒子群算法优化

为了优化目前粒子群算法比较容易陷入局部最优、后期收敛过慢等的缺陷，在本文提出了一种改进惯性权重参数来优化算法的方法.其中结合了差分进化算法中的变异算子的操作来提升算法的自适应并且对算法的速度和搜索空间进行边界限制以防止粒子跳出所规定的搜索空间.选择相应的测试函数，使用Matlab软件将提出的改进算法与其他两种算法进行仿真实验对比，结果表明，本文所提出的算法在后期收敛速度以及取得适应度值的稳定性上有一定的提升.

Particle Swarm Optimization with Dynamic Adjustment of Inertia Weight

In order to optimize the current Particle Swarm Optimization (PSO) algorithm, which is easy to fall into local optimum, slow convergence and another faults, this study proposes an improved inertia weight parameter method to optimize the algorithm. Combining the operation of mutation operator in Differential Evolution (DE) algorithm to improve the self-adaptation of the algorithm and limit the speed and search space of the algorithm to prevent particles from jumping out of the prescribed search space. Choose the corresponding test function and compare the improved algorithm with the other two algorithms by using Matlab software. The results show that the improved algorithm has a certain improvement in the convergence speed and the stability of fitness value.