• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Brazilian Conference on Intelligent Systems >A New Hyper-Heuristic Based on a Restless Multi-armed Bandit for Multi-objective Optimization
【24h】

A New Hyper-Heuristic Based on a Restless Multi-armed Bandit for Multi-objective Optimization

机译：一种基于不停歇多臂土匪的多目标优化的超启发式算法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Hyper-Heuristic is a high-level methodology that automates the selection or generation of other heuristics. Despite their success, there are only a few hyper-heuristics developed for multi-objective optimization. Our approach, namely MOEA/DRMAB, is a multi-objective selection hyper-heuristic that expands the MOEA/D framework. It uses an innovative Restless Multi- Armed Bandit (MAB) to determine the low level heuristic (Differential Evolution mutation strategy) that should be applied to each individual during a MOEA/D execution. The advantage of using a Restless MAB is that it is able to better model and tackle the operators dynamic behavior. We tested MOEA/D-RMAB in a well established set of 10 instances from the CEC 2009 MOEA Competition. Pareto compliant indicators and Mann- Whitney statistical tests are applied to evaluate the algorithm performances. Results show that MOEA/D-RMAB outperforms some important multi-objective optimization algorithms, including MOEA/D-FRRMAB (a prominent MOEA/D variation which uses a classical MAB operator selection), becoming a promising multi-objective Hyper-Heuristic.
机译：Hyper-Heuristic是一种高级方法，可自动选择或生成其他启发式方法。尽管取得了成功，但只有少数超启发式方法可用于多目标优化。我们的方法，即MOEA / DRMAB，是一种多目标选择超启发式方法，可扩展MOEA / D框架。它使用创新的躁动多臂匪徒（MAB）来确定在执行MOEA / D期间应应用于每个人的低级启发式（差异进化突变策略）。使用Restless MAB的优势在于它能够更好地建模并解决操作员的动态行为。我们在来自CEC 2009 MOEA竞赛的10个实例中建立了一套完善的MOEA / D-RMAB。使用帕累托兼容指标和曼惠特尼统计测试来评估算法性能。结果表明，MOEA / D-RMAB优于某些重要的多目标优化算法，包括MOEA / D-FRRMAB（使用经典MAB算子选择的突出MOEA / D变体），成为有希望的多目标Hyper-Heuristic。

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