• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm
【24h】

Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm

机译：使用可变邻域搜索算法的串并联系统冗余分配

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

This paper presents a meta-heuristic algorithm, variable neighborhood search (VNS), to the redundancy allocation problem (RAP). The RAP, an NP-hard problem, has attracted the attention of much prior research, generally in a restricted form where each subsystem must consist of identical components. The newer meta-heuristic methods overcome this limitation and offer a practical way to solve large instances of the relaxed RAP where different components can be used in parallel. Authors' previously published work has shown promise for the variable neighborhood descent (VND) method, the simplest version among VNS variations, on RAP. The variable neighborhood search method itself has not been used in reliability design, yet it is a method that fits those combinatorial problems with potential neighborhood structures, as in the case of the RAP. Therefore, authors further extended their work to develop a VNS algorithm for the RAP and tested a set of well-known benchmark problems from the literature. Results on 33 test instances ranging from less to severely constrained conditions show that the variable neighborhood search method improves the performance of VND and provides a competitive solution quality at economically computational expense in comparison with the best-known heuristics including ant colony optimization, genetic algorithm, and tabu search.
机译：针对冗余分配问题（RAP），本文提出了一种元启发式算法，即可变邻域搜索（VNS）。 RAP是一个NP难题，已经引起了很多先前研究的关注，通常是以受限的形式出现的，其中每个子系统必须包含相同的组件。较新的元启发式方法克服了此限制，并提供了一种解决大型RAP实例的实用方法，在该实例中可以并行使用不同的组件。作者先前发表的著作显示了在RAP上使用可变邻域下降（VND）方法（VNS变体中最简单的版本）的希望。变量邻域搜索方法本身并未用于可靠性设计中，但它是一种将那些组合问题与潜在邻域结构进行拟合的方法，例如RAP。因此，作者进一步扩展了他们的工作，为RAP开发了VNS算法，并从文献中测试了一系列众所周知的基准问题。 33个测试实例的结果范围从较小到严重受限，结果表明，与最著名的启发式算法（包括蚁群优化，遗传算法，和禁忌搜索。

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