基于改进收缩因子的粒子群优化算法

PSO算法寻优性能优劣受速度更新公式影响,过快的收敛速度可能使算法错过全局最优解;过慢的收敛速度可能会使算法陷于局部最优解。针对该问题,文中提出了一种基于改进压缩因子的PSO优化算法,即FPSO。通过引入压缩因子方程,改进了速度迭代公式,减少了因学习因子设置不当对算法造成的影响。新的调节机制既保证了PSO算法的收敛性能,也削弱了速度边界对算法的影响。最后,选取5个经典函数对算法性能进行测试。测试结果表明,与传统PSO算法相比,文中算法提高了全局收敛能力,缩短了收敛时间。

PSO Algorithm Based on Improved Contraction Factor

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.