一种基于粒子对称分布多样性的PSO算法

粒子群算法(PSO)在演化的过程中种群多样性越来越差，容易陷于局部最优。为了克服这一缺点，提出一种基于粒子对称分布多样性的改进PSO算法(sdPSO)。对粒子在空间分布的研究发现，粒子在最优解周围更对称的分布可大大提高算法收敛到全局最优解的概率。提出一种种群多样性函数表示方法，并在标准粒子群算法中引入多样性调节算法。由于种群多样性被不断调整，粒子在空间中的分布在对称与非对称之间反复变换，使得改进算法能搜索到更广泛的区域。通过benchmark函数实验仿真，改进sdPSO算法与标准粒子群算法相比，能达到更高的收敛精度。

An Improved PSO Based on Diversity of Particle Symmetrical Distribution

Particle swarm optimization (PSO) is easy to fall into the local optimum as the diversity of population gets worse and worse during the evolution. To overcome the shortcoming, an improved PSO based on the diversity of particle symmetrical distribution (sdPSO) is developed. Over the research of the spatial distribution of particles, it can be found that the convergence probability to the global optimum solution is greatly improved with more symmetrical particle distribution surrounding the optimum solution of particles. A diversity population function is proposed and an adjustment algorithm for the diversity is introduced into the basic PSO. The spatial distribution of particles varies between asymmetry and symmetry repeatedly while the population diversity is adjusted continually, which make the improved algorithm search in a wider range. The simulation results show that the improved sdPSO algorithm achieves better convergence precision than the basic PSO by the experiment of benchmark functions.