引入云模型的改进人口迁移算法

针对人口迁移算法搜索速度较慢，易陷入局部最优的缺点，改进了算法思想，简化了算法步骤，并利用云模型云滴的随机性和稳定倾向性的特点，由基本云发生器实现人口流动操作，提出了一种云人口迁移算法，用于求解具有变量边界约束的非线性的复杂函数最优化问题。实验仿真结果表明，云人口迁移算法具有计算精度较高，搜索速度较快等特点。

Cloud- model- based population migration algorithm

Population migration algorithm （PMA） easily gets stuck at a local optimum, and often has slow convergent speed. A novel population migration algorithm, cloud -model -based population migration algorithm （CPMA） is proposed. CPMA used for optimizing complex nonlinear function is based on both the idea of improved population flow and the properties of randomness and stable tendency of a normal cloud model. A basic normal cloud generator is used as the population flow operator and its algorithm step is simplified. Finally, the numerical experiment results show that this method not only can effectively locate the global optimum, but also have a rather high convergence speed.