基于学习与竞争的改进PSO算法研究

针对普通PSO算法收敛速率慢,难以收敛到全局最优解的问题,提出了一种基于学习与竞争的改进PSO算法.该算法通过将种群内部学习和竞争的思想与PSO算法相结合,让种群中个体通过竞争和学习策略来替代原有的PSO算法迭代公式.该方法在不增加PSO算法计算复杂度的基础上,能够克服基本PSO算法的不足.最后基于动态系统的稳定性分析理论,给出了该PSO算法收敛性的证明.在7种不同的测试函数上对改进后的算法进行了实验测试.实验结果表明该改进算法比传统的PSO算法有着更好的搜索精度.结果证明,新算法比普通的PSO算法具有更高的搜索精度和较低的时间复杂度.改进算法求解函数优化问题更加有效,收敛速率更快.

On a Modified PSO Algorithm Based on Learning and Competitiveness

As PSO algorithm for the general rate of convergence is slow and difficult to converge to the global optimal solution of the problem, a competition-based learning and improvement of PSO has been proposed in this paper. The algorithm by studying populations and competitive internal thoughts and PSO algorithm combined population of individuals so that by competing with each other and learning strategies to replace the original PSO algorithm iteration formula. This method based on PSO algorithm without increasing the computational complexity, can overcome the lack of basic PSO algorithm. Enhance searching precision. On seven different test functions for improved algorithm experimentally tested. Experimental results show that the improved algorithm than the traditional PSO algorithm has better search accuracy. The results prove that the new algorithm has higher precision and lower search time complexity than conventional PSO algorithm. The improved algorithm for solving function optimization problems more efficiently, faster convergence rate.