多目标优化的随机变邻域搜索
Randomised Variable Neighbourhood Search for Multi Objective Optimisation
摘要：
各种局部搜索方法最近已被应用于多目标下的机器调度问题。他们首先考虑的是确定一组帕累托最优方案。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。在这种情况下仍然不清楚，如何相互依存的健身景观影响问题的解决。研究了多目标流水车间调度问题中的邻域搜索算子。实验已经进行了12个不同的组合的标准。为了得出确切的结论，选择了最佳解决方案已知的小问题实例。统计测试表明，没有一个单一的邻域算子是能够平等地识别所有的帕累托最优方案。然而，已经获得了显着的改善，通过混合使用随机变量邻域搜索技术的解决方案算法。
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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