期刊文献+

函数发生机构优化综合的改进差分进化算法 被引量:4

Improved differential evolution algorithm for optimal synthesis of function generating linkages
在线阅读 下载PDF
导出
摘要 标准差分(SDE)算法具有算法简单、控制参数少、易于实现等优点,但容易发生早熟收敛。针对此缺点,提出一种改进的差分进化(IDE)算法。建立了铰链四杆函数发生机构近似运动综合的无约束优化模型,并应用IDE算法求解该优化问题。数值实例表明,IDE算法可以较好地克服早熟收敛问题,且能快速求出铰链四杆函数发生机构近似运动综合的优化解。 The standard differential evolution (SDE) algorithm has the advantages of simplicity, few control parameters required, and easily be used, but has the disadvantage of premature convergence. The improved DE (IDE) algorithm is proposed to overcome the demerits of the SDE algorithm. An unconstrained opti- mization model is presented to approximately synthesize function generators of pin-jointed 4-bar linkages. And then, the IDE algo- rithm is utilized to solve this optima/problem for function synthesis. A numerical result shows that the IDE algorithm can overcome the problem of premature convergence, and quickly approach to optimal solutions for approximate kinematic synthesis of function generators.
出处 《机械设计》 CSCD 北大核心 2010年第3期5-9,共5页 Journal of Machine Design
基金 四川省应用基础研究计划资助项目(2008JY0163)
关键词 铰链四杆机构 函数发生 优化综合 差分进化算法 pin-jointed 4-bar linkage function generating optimal synthesis differential evolution algorithm
  • 相关文献

参考文献11

二级参考文献141

共引文献452

同被引文献41

  • 1孙丕忠,夏智勋,赵建民.约束处理策略对遗传算法优化性能的影响[J].固体火箭技术,2005,28(4):235-237. 被引量:6
  • 2赵新超.非均匀演化算法及其应用[J].计算机学报,2006,29(10):1856-1861. 被引量:5
  • 3吴燕玲,马修水.基于改进差分进化的起重机主梁优化设计[J].现代制造工程,2007(3):126-128. 被引量:6
  • 4刘波,王凌,金以慧.差分进化算法研究进展[J].控制与决策,2007,22(7):721-729. 被引量:294
  • 5Yost R S, Uehara Gand Fox R L. Geostatistical analysis of soil chemical properties of large land areas. I. Semivariograms [J]. Soil Sci. Soc. Am. J. ,1982,46:1 028-1 037.
  • 6Ping J L, Dobermann A. Variation in the precision of soil organic carbon maps due to different laboratory and spatial prediction methods [ J ]. Soil Science,2006,171 ( 5 ) :374-387.
  • 7Han S, Schneider M S, Evans R G. Evaluating cokriging for improving soil nutrient sampling efficiency[ J ]. Transactions of the ASAE, 2003,46 ( 3 ) :845-849.
  • 8Yang Shengqiang, Sun Yan, Chen Zuyun, et al. Establishment of greyneural network forecasting model of coal and gas outburst [ J ]. Procedia Earth and Planetary Science, 2009,1 ( 1 ) :148-153.
  • 9Liu Y P, Wu M G, Qian J X. Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network [J]. Thermoehimica Acta, 2007,454 ( 1 ) : 64 -68.
  • 10Yang Min, Wang Yunjia, Cheng Yuanping. An incorporate genetic algorithm based back propagation neural network model for coal and gas outburst intensity prediction [ J ]. Procedia Earth and Planetary Science,2009,1 ( 1 ) : 1 285 - 1 292.

引证文献4

二级引证文献10

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部