摘要
Abstract
In order to overcome the premature convergence and low stability of the particle swarm optimization algorithm in the process of evolution, a particle swarm optimization algorithm with adaptive adjusting is introduced. In the algorithm, the uniformity distribution of the objective function fitness, which can maintain the diversity of the population, is adaptively adjusted. The strategy improves the global optimization ability of the algorithm, in which, the threshold constant can be avoided as much as possible,which may profoundly influence the stability of the algorithm. Moreover, the inertia weight with adaptive periodic mutation is proposed to update the velocity of the particles, which can improve the capability of local search and the stability of the algorithm. The improved algorithm is tested via a few benchmark functions in some simulations, and the experiment results show that it has high global convergence precision and well stability, and can also prevent early maturity.关键词
粒子群优化/多样性/均匀分布/周期性变异Key words
particle swarm optimization/ diversity/ uniformity distribution/ periodic mutation分类
信息技术与安全科学
