...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied Mathematical Modelling >Grouping evolution strategies: An effective approach for grouping problems
【24h】

Grouping evolution strategies: An effective approach for grouping problems

机译：分组演化策略：分组问题的有效方法

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

Many combinatorial optimization problems include a grouping (or assignment) phase wherein a set of items are partitioned into disjoint groups or sets. Introduced in 1994, the grouping genetic algorithm (GGA) is the most established heuristic for grouping problems which exploits the structural information along with the grouping nature of these problems to steer the search process. The aim of this paper is to evaluate the grouping version of the classic evolution strategies (ES) which originally maintain the well-known Gaussian mutation, recombination and selection operators for optimizing non-linear real-valued functions. Introducing the grouping evolution strategies (GES) to optimize the grouping problems that are intrinsically discrete, requests for developing a new mutation operator which works with groups of items rather than scalars and is respondent to the structure of grouping problems. As a source of variation, GES employs a mutation operator which shares a same rationale with the original ES mutation in the way that it works in continuous space while the consequences are used in discrete search space. A two phase heuristic procedure is developed to generate a complete feasible solution from the output of the mutation process. An extensive comparative study is conducted to evaluate the performance of GES versus GGA and GPSO (a recently proposed grouping particle swarm optimization algorithm) on test problem instances of the single batch-processing machine scheduling problem and the bin-packing problem. While these problems share exactly a same grouping structure and the performance of GES on both problems is reliable, switching from one problem to another deteriorates the performance of GGA. Though such a deficiency is not observed in the performance of GPSO, it is still inferior to GES on the single batch-processing machine scheduling test problem instances. Beside such empirical outcomes, the paper conveys a number of core strengths that the design of GES supports them but the design of GGA does not address them.
机译：许多组合优化问题包括分组（或分配）阶段，其中一组项目被划分为不相交的组或集合。分组遗传算法（GGA）于1994年推出，是针对分组问题的最成熟的启发式算法，它利用结构信息以及这些问题的分组性质来指导搜索过程。本文的目的是评估经典进化策略（ES）的分组版本，该策略最初保留了众所周知的高斯变异，重组和选择算子，以优化非线性实值函数。引入分组演化策略（GES）来优化本质上离散的分组问题，要求开发一种新的变异算子，该算子可用于项目组而不是标量，并且能够响应分组问题的结构。作为变异的来源，GES使用了一种变异算子，该变异算子与原始ES变异具有相同的原理，即它可以在连续空间中工作，而其后果用于离散搜索空间中。开发了一个两阶段启发式过程，以根据突变过程的输出生成一个完整可行的解决方案。进行了广泛的比较研究，以评估GES与GGA和GPSO（最近提出的分组粒子群优化算法）在单批处理机调度问题和装箱问题的测试问题实例上的性能。尽管这些问题共享完全相同的分组结构，并且GES在这两个问题上的性能都是可靠的，但从一个问题切换到另一个问题会降低GGA的性能。尽管在GPSO的性能中未发现这种缺陷，但在单个批处理机调度测试问题实例上，它仍不及GES。除了这些经验结果，本文还传达了GES的设计支持它们的许多核心优势，但GGA的设计并未解决这些优势。

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