基于分类思想的改进粒子群优化算法

An improved particle swarm optimization algorithm based on classification

针对粒子群算法存在收敛速度慢、收敛精度低且易收敛到局部极值的问题,提出一种基于分类思想的粒子群改进算法。该算法将粒子适度值和适度值均值做差与适度值标准差进行比较,从而将粒子所在区域划分为拒绝域、亲近域、合理域。根据不同区域中粒子的特点选取不同惯性权重和学习因子,使粒子高效地选择自身经验或种群经验,合理增强或减弱粒子全局搜索能力和局部搜索能力。数值实验结果表明,与其他粒子群改进算法相比,新的分类粒子群算法有效加快了粒子的收敛速度,提高了算法的收敛精度,有效改善了算法寻优性能。

In order to solve the problems of slow convergence speed,low convergence precision and easy convergence to local extremum,an improved particle swarm optimization algorithm based on classification is proposed. The difference between the moderate value and the mean of moderate value is compared with the standard deviation of moderate value in this algorithm, then the region where the particles are located is divided into rejection domain,close proximity domain,and reasonable domain. According to the characteristics of particles in different regions,different inertia weights and learning factors are selected to ensure that the particles can efficiently select their own experience or population experience,and reasonably enhance or weaken the global search ability and the local search ability of the particles. The numerical results show that,in comparison with other particle swarm optimization algorithms,the proposed particle swarm optimization algorithm can more effectively accelerate the convergence speed of particles,and improve the convergence precision and optimization performance of the algorithm.