• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Intelligent Manufacturing >Scalable platforms using ant colony optimization
【24h】

Scalable platforms using ant colony optimization

机译：使用蚁群优化的可扩展平台

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.
机译：大规模定制需要在客户端增加产品种类，而在制造商端减少零件种类。产品平台的概念已经成功地实现了这一目标。产品平台形成的一种流行方法是缩放一个或多个称为缩放变量的设计变量。需要有效的优化方法来确定缩放变量的正确值。本文提出了一种基于图的优化方法，称为可扩展平台，使用蚁群优化（SPACO）方法来识别缩放变量的适当值。在基于图的表示中，每个节点表示设计变量的值的子范围。该应用程序在节点选择中包含了多重性的概念，因为存在多个节点，这些节点对应于给定设计变量的离散值。在SPACO方法中，总体决策是由称为蚂蚁的简单计算代理在多次迭代中做出的累积决策的结果。空间搜索技术最初是在整个搜索空间上以随机搜索技术开始的，随着解决方案的成熟，它逐渐变成了自动催化（正反馈）概率搜索技术。我们使用一系列在文献中广泛引用的通用电动机，以测试该方法的有效性。与文献报道的结果相比，我们的仿真结果证明SPACO方法是一种可行的优化方法，用于确定可伸缩平台的设计变量的值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号