• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mathematical Problems in Engineering >The Evolutionary Algorithm to Find Robust Pareto-Optimal Solutions over Time
【24h】

The Evolutionary Algorithm to Find Robust Pareto-Optimal Solutions over Time

机译：随着时间的推移找到鲁棒的帕累托最优解的进化算法

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

In dynamic multiobjective optimization problems, the environmental parameters change over time, which makes the true pareto fronts shifted. So far, most works of research on dynamic multiobjective optimization methods have concentrated on detecting the changed environment and triggering the population based optimization methods so as to track the moving pareto fronts over time. Yet, in many real-world applications, it is not necessary to find the optimal nondominant solutions in each dynamic environment. To solve this weakness, a novel method called robust pareto-optimal solution over time is proposed. It is in fact to replace the optimal pareto front at each time-varying moment with the series of robust pareto-optimal solutions. This means that each robust solution can fit for more than one time-varying moment. Two metrics, including the average survival time and average robust generational distance, are present to measure the robustness of the robust pareto solution set. Another contribution is to construct the algorithm framework searching for robust pareto-optimal solutions over time based on the survival time. Experimental results indicate that this definition is a more practical and time-saving method of addressing dynamic multiobjective optimization problems changing over time.
机译：在动态多目标优化问题中，环境参数会随时间变化，从而使真正的Pareto前沿发生变化。到目前为止，关于动态多目标优化方法的大多数研究工作都集中在检测变化的环境并触发基于种群的优化方法，以便随时间推移跟踪运动的前沿。但是，在许多实际应用中，没有必要在每个动态环境中找到最佳的非主流解决方案。为了解决这一弱点，提出了一种新的方法，称为“鲁棒的最佳局部最优解”。实际上，在每个随时间变化的时刻都用一系列健壮的最优对策替换了最优最优对策。这意味着每个健壮的解决方案可以适应多个时变时刻。存在两个指标，包括平均生存时间和平均健壮世代距离，以衡量健壮pareto解决方案集的健壮性。另一个贡献是构建了基于生存时间在一段时间内搜索健壮的最优解决方案的算法框架。实验结果表明，该定义是一种解决随时间变化的动态多目标优化问题的实用且省时的方法。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2015年第7期|814210.1-814210.18|共18页
  • 作者

    Chen Meirong; Guo Yinan; Liu Haiyuan; Wang Chun;

  • 作者单位

    China Univ Min & Technol, Coll Sci, Xuzhou 221116, Peoples R China.;

    China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China.;

    China Univ Min & Technol, Coll Sci, Xuzhou 221116, Peoples R China.;

    China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221116, Peoples R China.;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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