摘要
提出一种求解约束优化问题的改进粒子群优化算法.该算法更多地考虑了当前全局最优粒子和个体最优粒子对粒子群搜索能力的影响,对速度更新公式做了改进;然后利用修正的可行基规则来更新个体极值和全局极值,从而引导不可行粒子尽可能到达可行的区域,以增加种群的多样性和提高全局搜索能力.数值实验表明,该算法是有效、稳定且计算精度高的全局优化算法.
An improved particle swarm optimization algorithm was proposed for solving constrained optimization problems.In this algorithm,the influence of current global optimal particle and current individual optimal particles on particle swarm search ability was much more take into considering,and the updated velocity formula was modified,Then the modified feasibility-based rule was used to update the individual optimal and global maximum,so that the infeasible particles were guided to become feasible ones in order to increase the diversity of population and improve the global search ability.Numerical simulation showed that this algorithm was a global optimization algorithm with higher efficiency,stability,and computation accuracy.
出处
《兰州理工大学学报》
CAS
北大核心
2011年第4期84-89,共6页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(60962006)
宁夏自然科学基金(NZ0848)
关键词
全局优化
约束优化
粒子群优化
惩罚函数
可行基规则
global optimization
constrained optimization
particle swarm optimization
penalty function
feasibility-based rule
