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Automatic Design of Hybrid Stochastic Local Search Algorithms

机译:混合随机本地搜索算法的自动设计

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Many stochastic local search (SLS) methods rely on the manipulation of single solutions at each of the search steps. Examples are iterative improvement, iterated local search, simulated annealing, variable neighborhood search, and iterated greedy. These SLS methods are the basis of many state-of-the-art algorithms for hard combinatorial optimization problems. Often, several of these SLS methods are combined with each other to improve performance. We propose here a practical, unified structure that encompasses several such SLS methods. The proposed structure is unified because it integrates these metaheuristics into a single structure from which we can not only instantiate each of them, but we also can generate complex combinations and variants. Moreover, the structure is practical since we propose a method to instantiate actual algorithms for practical problems in a semi-automatic fashion. The method presented in this work implements a general local search structure as a grammar; an instantiation of such a grammar is a program that can be compiled into executable form. We propose to find the appropriate grammar instantiation for a particular problem by means of automatic configuration. The result is a semi-automatic system that, with little human effort, is able to generate powerful hybrid SLS algorithms.
机译:许多随机本地搜索(SLS)方法依赖于在每个搜索步骤中操作单个解决方案。示例是迭代改进,迭代本地搜索,模拟退火,可变邻域搜索,并迭代贪婪。这些SLS方法是许多最先进算法的硬组合优化问题的基础。通常,这些SLS方法中的几种相互组合以提高性能。我们在这里提出了一种实用的统一结构,包括几种这样的SLS方法。拟议的结构是统一的,因为它将这些陨素的学分集成到一个结构中,我们不仅可以实例化它们,而且我们还可以生成复杂的组合和变体。此外,该结构是实用的,因为我们提出了一种以半自动方式实例化实际问题实际算法的方法。本工作中呈现的方法将一般的本地搜索结构实现为语法;这种语法的实例化是可以编制到可执行形式的程序。我们建议通过自动配置找到特定问题的适当语法实例化。结果是一个半自动系统,具有很少的人力努力,能够产生强大的混合体SLS算法。

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