| GAPSO：一种高效的遗传粒子混合算法及其应用 |
| |
| 引用本文： | 彭晓波,桂卫华,黄志武,胡志坤,李勇刚.GAPSO：一种高效的遗传粒子混合算法及其应用[J].系统仿真学报,2008,20(18). |
| |
| 作者姓名： | 彭晓波 桂卫华 黄志武 胡志坤 李勇刚 |
| |
| 作者单位： | 中南大学信息科学与工程学院 |
| |
| 基金项目： | 国家自然科学基金，国家重点基础研究发展计划(973计划)，湖南省自然科学基金，国家发改委专项，中国博士后科学基金 |
| |
| 摘 要： | 在粒子群算法和遗传算法融合的基础上提出了一种新的算法(GAPSO).该算法模仿自然界的个体成熟过程,对遗传算法中的每一代群体中的优秀个体,采用粒子群算法获得进一步的提高,使算法获得比遗传算法和粒子群算法更加好的优化效果.在FCRNN设计应用中表明该算法确实比遗传算法和粒子群算法有更加好的效果.
|
| 关 键 词： | 遗传算法 粒子群算法 |
GAPSO: Effective Genetic Particle Swarm Algorithm and Its Application |
| |
| PENG Xiao-bo,GUI Wei-hua,HUANG Zhi-wu,HU Zhi-kun,LI Yong-gang.GAPSO: Effective Genetic Particle Swarm Algorithm and Its Application[J].Journal of System Simulation,2008,20(18). |
| |
| Authors: | PENG Xiao-bo GUI Wei-hua HUANG Zhi-wu HU Zhi-kun LI Yong-gang |
| |
| Abstract: | Based on the genetic algorithm and swam optimization algorithm, a new algorithm (GAPSO) was proposed. This new algorithm mimics the mature process in nature. Optimal individuals of every generation in genetic algorithm get the further improvement by PSO algorithm. Optimization effect of this new algorithm is better than the genetic algorithm and swam optimization algorithm. Application in FCENN design indicates that this new algorithm is indeed better than the genetic algorithm and swam optimization algorithm. |
| |
| Keywords: | GAPSO FCRNN |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
