摘要
粒子群算法是一种基于群体智能的随机并行算法,它在很多优化问题中都得到了比较好的应用。本文针对粒子群容易陷入局部最优解,提出了一种加入创新粒子的粒子群,实验模拟结果表明加入创新粒子的粒子群有更好的结果和收敛速度。
Particle Swarm Optimization （PSO） is a swarm intelligence based on stochastic parallel algorithm, which in many optimization problems have been a better application. In this paper, particle swarm easily trapped into local optimal solution, a new PSO by adding innovative particles is proposed. Experimental simulation results show that it has better results and the convergence rate, I believe that in other specific optimization problems by adding innovative particles will have a very good performance of PSO.
出处
《数学理论与应用》
2010年第1期14-17,共4页
Mathematical Theory and Applications
关键词
粒子群
自适应
非线性
Particle Swarm Optimization Innovation particles Local solution
