摘要翻译：
近年来，各种局部搜索方法被应用于多目标下的机器调度问题。他们首要考虑的是确定帕累托最优方案集。成功解决这些问题的一个重要方面在于定义适当的邻域结构。在这一背景下，尚不清楚的是，健康状况中的相互依赖性如何影响问题的解决。研究了多目标流水车间调度的邻域搜索算子。用12种不同的标准组合进行了实验。为了得到精确的结论，选择了已知最优解的小问题实例。统计检验表明，任何一个邻域算子都不能平等地识别所有的Pareto最优方案。然而，通过混合使用随机变量邻域搜索技术的求解算法，已经获得了显著的改进。
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英文标题：
《Randomised Variable Neighbourhood Search for Multi Objective
  Optimisation》
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作者：
Martin Josef Geiger
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最新提交年份：
2008
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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：
  Various local search approaches have recently been applied to machine scheduling problems under multiple objectives. Their foremost consideration is the identification of the set of Pareto optimal alternatives. An important aspect of successfully solving these problems lies in the definition of an appropriate neighbourhood structure. Unclear in this context remains, how interdependencies within the fitness landscape affect the resolution of the problem.   The paper presents a study of neighbourhood search operators for multiple objective flow shop scheduling. Experiments have been carried out with twelve different combinations of criteria. To derive exact conclusions, small problem instances, for which the optimal solutions are known, have been chosen. Statistical tests show that no single neighbourhood operator is able to equally identify all Pareto optimal alternatives. Significant improvements however have been obtained by hybridising the solution algorithm using a randomised variable neighbourhood search technique.
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PDF链接：
https://arxiv.org/pdf/0809.0271