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摘要翻译：
本文给出了一个求解多目标优化问题的局部搜索元启发式的提出及其应用。它基于启发式搜索的两个主要原则，即通过可变邻域进行强化，以及通过在搜索空间的有利区域中的扰动和连续迭代进行多样化。在多目标条件下的置换流水车间调度问题上成功地验证了该概念，并与其他局部搜索方法进行了比较。虽然所获得的结果在质量方面令人鼓舞，但该方法的另一个积极属性是它的简单性，因为它只需要设置很少的参数。
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英文标题：
《Improvements for multi-objective flow shop scheduling by Pareto Iterated
Local Search》
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作者：
Martin Josef Geiger
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最新提交年份：
2009
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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：
The article describes the proposition and application of a local search metaheuristic for multi-objective optimization problems. It is based on two main principles of heuristic search, intensification through variable neighborhoods, and diversification through perturbations and successive iterations in favorable regions of the search space. The concept is successfully tested on permutation flow shop scheduling problems under multiple objectives and compared to other local search approaches. While the obtained results are encouraging in terms of their quality, another positive attribute of the approach is its simplicity as it does require the setting of only very few parameters.
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PDF链接：
https://arxiv.org/pdf/0907.2993
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