高级贝叶斯优化算法在分解问题中的应用

J. Schwarz, Jiri Ocenasek, J. Jaros
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引用次数: 2

摘要

本文讨论了贝叶斯优化算法(BOA)及其高级变体在求解复杂np -完全组合优化问题中的应用。我们重点研究了超图分区问题和多处理器调度问题,这两类问题属于经常被解决的分解任务。目的之一是利用这些问题对最近提出的混合贝叶斯优化算法(MBOA)的性能与其他几种基于概率模型估计和抽样的进化算法的性能进行实验比较。我们还提出利用超图和任务图结构的先验知识来提高收敛速度和解的质量。对基于知识的MBOA算法在多处理机调度问题上的性能进行了实证研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced Bayesian optimization algorithms applied in decomposition problems
We deal with the usage of Bayesian optimization algorithm (BOA) and its advanced variants for solving complex NP-complete combinatorial optimization problems. We focus on the hypergraph-partitioning problem and multiprocessor scheduling problem, which belong to the class of frequently solved decomposition tasks. One of the goals is to use these problems to experimentally compare the performance of the recently proposed Mixed Bayesian optimization algorithm (MBOA) with the performance of several other evolutionary algorithms based on the estimation and sampling of probabilistic model. We also propose the utilization of prior knowledge about the structure of hypergraphs and task graphs to increase the convergence speed and the quality of solutions. The performance of knowledge based MBOA (KMBOA) algorithms on the multiprocessor scheduling problem is empirically investigated and confirmed.
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