基于高斯学习多峰延迟粒子群优化算法

为克服粒子群在解决多峰函数复杂问题时存在收敛速度慢和极易陷入局部最优值的缺点，提出了一种基于高斯学习多峰延迟粒子群混合算法。首先引入改进的高斯学习提高算法的收敛速度，然后在此基础上，针对4 种进化状态在算法中引入延迟因子避免局部最优问题。通过对6 个单峰多峰测试函数进行仿真实验，验证了GLPSO( Gaussian Learning PSO) 算法具有更好的收敛速度，同时验证了GLMDPSO( Gaussian Learning Multimodal Delayed PSO) 算法在处理多峰函数复杂问题时具备更好的全局搜寻能力。因此，改进算法在解决多峰函数寻优问题时可有效跳出停滞状态，提高收敛速度并具有较好的寻优能力。

Multimodal Delayed Particle Swarm Optimization Algorithm Based on Gaussian Learning#br#

In order to overcome the shortcomings of particle swarms in solving complex problems of multimodal functions，such as the convergence speed is slow and it is easy to fall into local optimal values． A multimodal delayed particle swarm optimization algorithm based on Gaussian learning is designed． Firstly，the improved Gaussian learning is introduced to improve the convergence speed of the algorithm． Then for the four evolution states，a delay factor is introduced into the algorithm to avoid the local optimal problem． Simulation experiments are performed by six unimodal multimodal test functions． It is verified that GLPSO ( Gaussian Learning PSO) has better convergence speed when dealing with six functions． And it is verified that the GLMDPSO ( Gaussian Learning Multimodal Delayed PSO ) algorithm has better global search ability when dealing with complex problems of multimodal functions． The improved algorithm solves the multimodal function optimization problem，the algorithm could effectively jump out of the stagnation state，improve the convergence speed and have better optimization ability．