基于高斯扰动和自然选择的改进粒子群优化算法

为了有效地平衡粒子群算法的全局与局部搜索性能,提出一种基于高斯扰动和自然选择的改进粒子群优化算法。该算法在采用简化粒子群优化算法的基础上,考虑到个体最优粒子间的相互影响,使用所有融入高斯扰动的个体最优的平均值代替每个粒子的个体最优值,并且借鉴自然选择中适者生存的进化机制提高算法优化性能;同时通过含有惯性权重停止阈值的自适应调节余弦函数递减策略来实现对惯性权重的非线性调整并采用异步变化调整策略来改善粒子的学习能力。仿真实验结果表明,所提算法在收敛速度和精度等方面均有提高,寻优性能优于近期文献中的几种改进的粒子群优化算法。

Improved particle swarm optimization algorithm based on Gaussian disturbance and natural selection

In order to effectively balance the global and local search performance of Particle Swarm Optimization (PSO) algorithm, an improved PSO algorithm based on Gaussian disturbance and natural selection (GDNSPSO) was proposed. Based on the simple PSO algorithm, the improved algorithm took into account the mutual influence among all individual best particles and replaced the individual best value of each particle with the mean value of them which contained Gaussian disturbance. And the evolution mechanism of survival of the fittest in natural selection was employed to improve the performance of algorithm. At the same time, the nonlinear adjustment of the inertia weight was adjusted by the cosine function with adaptive adjustment of the threshold of inertia weight and the adjustment strategy of the asynchronous change was used to improve the learning ability of the particles. The simulation results show that the GDNSPSO algorithm can improve the convergence speed and precision, and it is better than some recently proposed improved PSO algorithms.