基于改进收缩因子的粒子群优化算法.

The optimization performance of the PSO algorithm is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution; too slow convergence speed may cause the algorithm to fall into the local optimal solution. Aiming at this problem, this paper 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 on the algorithm caused by the improper setting of the learning factor is reduced. The new adjustment mechanism not only ensures the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classical functions are selected to test the performance of the 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. [ABSTRACT FROM AUTHOR]