用于分析基于高级局部搜索算法的多层局部最优网络
Multi-layer local optima networks for the analysis of advanced local search-based algorithms
摘要：
局部最优网络（LON）是一种基于特定邻域算子和局部搜索算法压缩特定组合优化问题的适应度景观的图模型。确定哪些景观特征以及如何影响搜索算法的有效性对于预测其性能和改进设计过程都是相关的。本文提出了多层LON的概念，以及探索这些模型的方法，旨在提取指标的健身景观分析。构建这样的模型，提取和分析它们的指标是将单邻域算子概率论的研究扩展到使用多个算子的更复杂的研究的初步步骤。因此，在本文中，我们研究了一个双层LON从组合问题的实例中使用位翻转和交换算子。首先，我们列举了NK景观模型的实例，并使用爬山启发式建立相应的LON。然后，使用LON指标，我们分析如何有效的搜索时，结合这两种策略。实验结果表明，有希望的结果，并证明了多层LON的能力，提供有用的信息，可用于元算法的基础上，如可变邻域搜索。
英文摘要：
A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improving the design process. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis. Constructing such models, extracting and analyzing their metrics are the preliminary steps into the direction of extending the study on single neighborhood operator heuristics to more sophisticated ones that use multiple operators. Therefore, in the present paper we investigate a twolayer LON obtained from instances of a combinatorial problem using bitflip and swap operators. First, we enumerate instances of NK-landscape model and use the hill climbing heuristic to build the corresponding LONs. Then, using LON metrics, we analyze how efficiently the search might be when combining both strategies. The experiments show promising results and demonstrate the ability of multi-layer LONs to provide useful information that could be used for in metaheuristics based on multiple operators such as Variable Neighborhood Search.
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