摘要
针对粒子群优化算法(PSO)固有的缺点,在研究标准的粒子群优化算法理论的基础上,提出了一种带交叉因子的改进的粒子群优化算法(MPSO),以解决算法的早熟收敛问题。该算法在搜索过程中引入了交叉因子,增加了粒子的多样性,克服了标准粒子群优化算法易陷入局部极优点的不足,并且算法有较快的收敛速度。该算法有较强的收敛性,还可以引入变异算子。将改进后的算法运用常见的几个测试函数进行了寻优仿真,仿真结果验证了带交叉因子的粒子群算法的可行性和有效性。
The modified particle swarm optimization algorithm with crossover operator （MPSO） is presented in this paper, which is based on the traditional PSO algorithm to overcome the inherent deficiency in particle swarm optimization algorithm such as premature convergence. The crossover operator is introduced in the searching process in order to avoid becoming trapped in a local optimum, increases the diversity of population, and shortens the convergence process to a certain degree. This modified PSO has better convergence property and mutation operator could also be introduced in this algorithm. The improved algorithm is applied to several examples. The simulation results show its feasibility and validity.
出处
《青岛科技大学学报（自然科学版）》
CAS
2008年第1期77-79,共3页
Journal of Qingdao University of Science and Technology:Natural Science Edition
关键词
粒子群优化算法
交叉因子
多维优化
particle swarm optimization algorithm
crossover operator
multidimensional optimization
