关键词: 粒子群优化算法, 全局最优解, 局部最优解, 压缩因子, 速度迭代, 收敛能力, 函数寻优, 时间优化

Abstract:

The performance of PSO algorithm optimization is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution, and the slow convergence speed may cause the algorithm to fall into the local optimal solution. To solve this problem, this study proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence of the improper setting of the learning factor on the algorithm is reduced. The new adjustment mechanism not only guarantees the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classic functions are selected to test the performance of the proposed algorithm. The test results show that compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time.

Key words: PSO, global optimal solution, locally optimal solution, compression factor, velocity iteration, convergence ability, function optimization, time optimization