• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Innovative Computing Information and Control >EVOLUTIONARY OPTIMIZATION IN DYNAMIC ENVIRONMENTS: BRINGING THE STRENGTHS OF DYNAMIC BAYESIAN NETWORKS INTO BAYESIAN OPTIMIZATION ALGORITHM
【24h】

EVOLUTIONARY OPTIMIZATION IN DYNAMIC ENVIRONMENTS: BRINGING THE STRENGTHS OF DYNAMIC BAYESIAN NETWORKS INTO BAYESIAN OPTIMIZATION ALGORITHM

机译：动态环境中的进化优化：将动态贝叶斯网络的强度纳入贝叶斯优化算法

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 this paper, a new evolutionary algorithm termed DBN-MBOA (Memory-based BOA with Dynamic Bayesian Networks) is proposed for the dynamic optimization. In DBN-MBOA, the knowledge obtained from previously solved problems is encoded in some structures called network translators. The network translators defined on non-stationary Dynamic Bayesian Networks (nsDBNs) describe the correlation between conditional dependencies of candidate solution variables before and after environmental changes. The network translators constructed for the changes are stored in memory. When any change occurs in the environment, a relevant network translator is retrieved from the memory and is used for modifying the dependencies of the current Bayesian network. In the retrieve stage, unlike existing memory-based methods, the relevant network translator is selected based on the characteristic of the change itself, not that of the new environmental state. Experimental results show that DBN-MBOA achieves better performance in random environments as well as cyclic environments.
机译：本文提出了一种新的进化算法DBN-MBOA（动态贝叶斯网络的基于内存的BOA）进行动态优化。在DBN-MBOA中，从先前解决的问题中获得的知识被编码在称为网络转换器的某些结构中。在非平稳动态贝叶斯网络（nsDBN）上定义的网络转换器描述环境变化前后候选解决方案变量的条件依存关系之间的相关性。为更改而构造的网络转换器存储在内存中。当环境发生任何变化时，都会从内存中检索相关的网络转换器，并将其用于修改当前贝叶斯网络的依赖性。在检索阶段，与现有的基于内存的方法不同，将根据更改本身的特征而不是新环境状态的特征来选择相关的网络转换器。实验结果表明，DBN-MBOA在随机环境和循环环境中均具有更好的性能。

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