吱吱轮优化

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摘要翻译：
我们描述了一种通用的优化方法，我们称之为“吱吱轮”优化(SWO)。在SWO中，使用贪婪算法构造一个解，然后对其进行分析，找出问题点，即那些如果改进，可能会提高目标函数得分的元素。分析的结果用于生成新的优先级，这些优先级决定贪婪算法构造下一个解决方案的顺序。这个构造/分析/确定优先级的循环一直持续到达到某个极限，或者找到一个可接受的解决方案。SWO可以被视为在两个搜索空间上运行：解决方案和优先级。连续的解决方案只是间接相关的，通过分析前一个解决方案的结果重新排序。类似地，通过构造和分析解决方案来生成连续的优先级。这种“耦合搜索”有一些有趣的性质，我们将讨论这些性质。我们报告了两个领域的令人鼓舞的实验结果，光缆制造中出现的调度问题和图着色问题。这些领域非常不同的事实支持了我们的说法，即SWO是一种用于优化的通用技术。
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英文标题：
《Squeaky Wheel Optimization》
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作者：
D. P. Clements, D. E. Joslin
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We describe a general approach to optimization which we term `Squeaky Wheel' Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. The results of the analysis are used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found. SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This `coupled search' has some interesting properties, which we discuss. We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that SWO is a general technique for optimization.
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PDF链接：
https://arxiv.org/pdf/1105.5454

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关键词：Optimization Intelligence Presentation Constructing Experimental algorithm 得分 制造 SWO 通用