| 一种改进的基于pareto解的多目标粒子群算法 |
| |
| 引用本文： | 李纬,张兴华.一种改进的基于pareto解的多目标粒子群算法[J].计算机仿真,2010,27(5):96-99. |
| |
| 作者姓名： | 李纬 张兴华 |
| |
| 作者单位： | 南京工业大学自动化学院,江苏,南京,210009 |
| |
| 基金项目： | 江苏省教育厅自然科学基金项目(06KJB510040) |
| |
| 摘 要： | 研究一种改进的多目标粒子群优化算法,算法采用精英归档策略,利用粒子的个体最优定位,通过Pareto支配关系更新全体粒子最优位置,由档案库中动态提供。根据Pareto支配关系来更新粒子的个体最优位置。使用非劣解目标的密度距离度量非劣解前端的均匀性,通过删除密度距离小的非劣解提高非劣解前端的均匀性。从归档中根据粒子的密度距离大小依照概率选取作为粒子的全局最优位置,以保持解的多样性。标准函数的仿真实验结果表明,所提算法能够获得大量且较均匀的非劣解,快速地收敛于Pareto最优解前端。
|
| 关 键 词： | 粒子群 多目标进化算法 最优化 密度距离 |
An Improved Multi-objective Particle Swarm Optimization Algorithm Based on Pareto |
| |
| LI Wei,ZHANG Xing-hua.An Improved Multi-objective Particle Swarm Optimization Algorithm Based on Pareto[J].Computer Simulation,2010,27(5):96-99. |
| |
| Authors: | LI Wei ZHANG Xing-hua |
| |
| Affiliation: | College of Automation/a>;Nanjing University of Technology/a>;Nanjing Jiangsu 210009/a>;China |
| |
| Abstract: | An improved multi-objective particle swarm optimization algorithm is proposed,in which elitism archived strategy is used,global best position is provided by non-dominated solutions in the archive and individual best position is updated based on Pareto dominance.The algorithm uses objective dynamic crowding to measure non-dominated solutions quality and employs the strategy of deleting low dynamic crowding solutions to enhance non-dominated solutions uniformity.According to the dynamic crowding distance prob... |
| |
| Keywords: | Particle swarm Multi-objective evolutionary algorithm Optimal Dynamic crowding |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
