图中的最小支配集是最小的顶点集,例如图的每个顶点都属于它,或者与该集的一个顶点相邻。该数学对象在与社交网络分析,无线网络设计,编码理论和数据挖掘等许多相关的应用中具有高度相关性。当给出顶点权重时,最小化主导集合的总权重会产生称为最小权重支配集合问题的问题变体。为了解决这个问题,我们引入了一个混合数学,将整数编程求解器与禁忌搜索结合起来。后者用于解决子问题,其中相对于搜索历史选择的决策变量的一小部分是自由的,而其他决定变量是固定的。此外,我们引入了自适应惩罚来促进搜索过程中中间不可行解的探索,增强了扰动和节点消除程序的算法,并开发了更丰富的邻域类。对各种实例类的广泛实验分析证明了算法的良好性能,并分析了每个组件在搜索成功中的贡献。
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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
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Scope and Purpose-Multi-way graph partition has important applications in parallel processing, VLSI design, and other resource allocation problems. Recently Lee, Park, and Kim proposed an (LPK) algorithm to solve this NP-hard problem based on the Kemighan-Lin approach. In this paper we investigate simulated annealing and tabu search approaches, and propose a new general approach, called stochastic probe, for combinatorial optimization to combine aggressive solution searches with stochastic solution searches. We use excessive experiments to demonstrate that our stochastic probe algorithm can significantly improve the solution quality of the LPK algorithm in comparable running time. Aktract-For a given graph G with vertex and edge weights, we partition the vertices into subsets to minimize the total weights for edges crossing the subsets under the constraint that the vertex weights are evenly distributed among the subsets. We adapt simulated annealing and tabu search to solve this problem based on a unique solution neighborhood design compromising aggressiveness and running time for each move. A new general optimization paradigm called stochastic probe is then proposed to integrate the advantages of both the aggressive searches and the stochastic searches. Extensive experimental study shows that all of our three new algorithms produce significantly better solutions than the LPK algorithm, and our stochastic probe algorithm always produces the best solution among all the four algorithms with a running time comparable with that for the LPK algorithm.
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During the last decade, a number of challenging applications in logistics, tourism and other fields were modelled as orienteering problems (OP). In the orienteering problem, a set of vertices is given, each with a score. The goal is to determine a path, limited in length, that visits some vertices and maximises the sum of the collected scores. In this paper, the literature about the orienteering problem and its applications is reviewed. The OP is formally described and many relevant variants are presented. All published exact solution approaches and (meta) heuristics are discussed and compared. Interesting open research questions concerning the OP conclude this paper.
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The identification of performance-optimizing parameter settings is animportant part of the development and application of algorithms. We describe anautomatic framework for this algorithm configuration problem. More formally, weprovide methods for optimizing a target algorithm's performance on a givenclass of problem instances by varying a set of ordinal and/or categoricalparameters. We review a family of local-search-based algorithm configurationprocedures and present novel techniques for accelerating them by adaptivelylimiting the time spent for evaluating individual configurations. We describethe results of a comprehensive experimental evaluation of our methods, based onthe configuration of prominent complete and incomplete algorithms for SAT. Wealso present what is, to our knowledge, the first published work onautomatically configuring the CPLEX mixed integer programming solver. All thealgorithms we considered had default parameter settings that were manuallyidentified with considerable effort. Nevertheless, using our automatedalgorithm configuration procedures, we achieved substantial and consistentperformance improvements.
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T his paper presents a genetic algorithm (GA) for solving the traveling salesman problem (TSP). To construct a powerful GA, we use edge assembly crossover (EAX) and make substantial enhancements to it: (i) local-ization of EAX together with its efficient implementation and (ii) the use of a local search procedure in EAX to determine good combinations of building blocks of parent solutions for generating even better offspring solutions from very high-quality parent solutions. In addition, we develop (iii) an innovative selection model for maintaining population diversity at a negligible computational cost. Experimental results on well-studied TSP benchmarks demonstrate that the proposed GA outperforms state-of-the-art heuristic algorithms in finding very high-quality solutions on instances with up to 200,000 cities. In contrast to the state-of-the-art TSP heuristics, which are all based on the Lin-Kernighan (LK) algorithm, our GA achieves top performance without using an LK-based algorithm.
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本文研究了由机器人代表的先验知识环境中的多机器人搜索静止物体的两种变体。第一个是旅行送货员问题的年龄化,其中允许在解决方案中使用不止一个交付员。类似地,第二个变体是图搜索问题的概括。提出了适用于这两个问题的新颖启发法,其进一步集成到第一路径第二路径中。对来自TSPLIB库的基准实例进行了一组计算实验。得到的结果表明,即使是独立的启发式算法也显着优于基于k-均值聚类的标准解决方案的结果质量和计算时间。这种集成方法进一步提高了独立启发式解决方案所能找到的解决方案,其成本更高,计算复杂度更高。
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参数控制旨在通过参数的动态选择来实现性能增益,所述参数确定底层优化算法的行为。在进化算法的背景下,该研究线长期以来一直由经验方法主导。随着近十年来运行时间分析的显着进步,参数控制问题已经可以进行理论研究。近年来已经获得了许多不同参数控制机构的运行时间结果。本书通过提出参数控制的更新分类方案,对这些工作进行了调查,并将其置于背景中。
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This paper presents the fundamental principles underlying tabu search as a strategy for combinatorial optimization problems. Tabu search has achieved impressive practical successes in applications ranging from scheduling and computer channel balancing to cluster analysis and space planning, and more recently has demonstrated its value in treating classical problems such as the traveling salesman and graph coloring problems. Nevertheless, the approach is still in its infancy, and a good deal remains to be discovered about its most effective forms of implementation and about the range of problems for which it is best suited. This paper undertakes to present the major ideas and findings to date, and to indicate challenges for future research. Part I of this study indicates the basic principles, ranging from the short-term memory process at the core of the search to the intermediate and long term memory processes for intensifying and diversifying the search. Included are illustrative data structures for implementing the tabu conditions (and associated aspiration criteria) that underlie these processes. Part I concludes with a discussion of probabilistic tabu search and a summary of computational experience for a variety of applications. Part I1 of this study (to appear in a subsequent issue) examines more advanced considerations, applying the basic ideas to special settings and outlining a dynamic move structure to insure finiteness. Part I1 also describes tabu search methods for solving mixed integer programming problems and gives a brief summary of additional practical experience, including the use of tabu search to guide other types of processes, such as those of neural networks. T a b u search is a strategy for solving combinatorial optimization problems whose applications range from graph theory and matroid settings to general pure and mixed integer programming problems. It is an adaptive procedure with the ability to make use of many other methods, such as linear programming algorithms and specialized heuristics, which it directs to overcome the limitations of local optimality. Tabu search has its origins in combinatorial procedures applied to nonlinear covering problems in the late 1970~,[~1 and subsequently applied to a diverse collection of problems ranging from scheduling and computer channel balancing to cluster analysis and space planning.'3.4,6.71 Latest research and computational comparisons involving traveling salesman, graph coloring, job shop flow sequencing, integrated circuit design and time tabling problems have likewise disclosed the ability of tabu search to obtain high quality solutions with modest computational effort, generally dominating alternative methods tested.['. 12-13. A recent independent development of several of its ideas[lol also has been applied successfully to maximum satisfiability problems.[''] Such applications, for problems ranging in size from hundreds to millions of variables, are elaborated in Section 10.
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无等待流水车间调度问题是classicpermutation flowshop问题的一种变体,附加约束条件是作业必须由连续的机器处理而无需等待时间。为了有效地解决这个NP难的组合优化问题,我们对优质解决方案的结构进行了分析。这一分析表明,No-Wait特异性赋予它们一个共同的结构:它们共享相同的子序列作业,我们称之为超级作业。在讨论识别这些超级作业的方法之后,我们提出了IG-SJ,这是一种利用经典置换流程中最先进算法的超级作业的算法,即着名的迭代贪婪(IG)算法。还提出了IG-SJ的迭代方法。在Taillard的实例上进行了实验。实验结果表明,开发超级工作是成功的,因为IG-SJ能够找到64个新的最佳解决方案。
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本调查介绍了社交网络中影响最大化问题的主要成果。这个问题在文献中得到了很好的研究,并且由于其最近的应用,其中一些目前在现场部署,它在科学界受到越来越多的关注。问题可以表述如下:给定一个图,每个节点具有影响其邻居的一定概率,选择一个子集,即网络中受影响的节点数量最大化。从这个模型开始,我们介绍主要已经实现的理论发展和计算结果,考虑了描述信息如何在整个网络中传播的不同扩散模型,信息来源的各种放置方式,以及在存在影响网络的不确定性时如何解决问题。最后,我们提出了一个主要的应用程序,它已经开发和部署了前面讨论过的工具和技术。
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图形核在过去十年中引起了很多关注,并且已经发展成为一个快速发展的结构化数据学习分支。在过去的20年中,在该领域发生的大量研究活动导致了数十个图形核的发展,每个聚焦图的特定结构属性。图形内核在各种领域都取得了成功,其中包括社交网络tobioinformatics。本次调查的目的是在图形内核上提供文献的统一视图。特别是,我们提供了各种图形内核的综合概述。此外,我们对公开可用的数据集中的几个内核进行了实验评估,并提供了一项比较研究。最后,我们讨论了图形内核的关键应用,并概述了仍有待解决的一些挑战。
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Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.
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群集中具有预定秩序的群集旅行推销员问题,是着名的旅行商问题的变体,是文学研究。在这个问题中,交付地点被分成具有不同紧急程度的集群,并且在较不紧急的地点之前必须访问更紧急的地点。然而,这可能导致在降低成本方面的低效路线。这种以优先级为导向的约束可以通过一个符合d的放宽优先级来放宽,该优先级提供了运输成本紧急程度之间的权衡。我们的研究提出了两种解决d-宽松优先级规则问题的方法。我们改进了文献中提出的数学公式,以构建精确求解方法。还引入了基于迭代局部搜索框架的元启发式方法,该方法具有问题定制操作符,以找到近似解。实验结果表明了我们的方法的有效性。
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当约束限制搜索空间的可行区域时,演化算法的性能会受到严重破坏。例如,虽然协方差矩阵自适应进化策略是无约束优化问题最有效的算法之一,但它不能轻易应用于约束优化问题。在这里,我们使用了Memetic Computing的概念,即。多个算法信息单元的和谐组合,以及人工进化的另一种抽象的可变性进化,为一种解决不等式约束优化问题的新方法。 Viability Evolution强调消除不满足可行性标准的解决方案,定义为目标和约束的边界。在搜索期间调整这些边界以基于协方差矩阵自适应演化策略向可行区域驱动本地搜索单元的人口。这些单元可以通过差分进化算子重新组合。对于我们的方法的性能至关重要,自适应调度器通过选择推进其中一个本地搜索单元和/或重新组合它们来在开发和探索之间切换。所提出的算法在各种基准和工程问题上都能胜过几种最先进的方法,包括解决方案的质量和所需的计算资源。
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The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact solutions in this framework are typically computationally intractable for all but the smallest problems. A well-known technique for speeding up POMDP solving involves performing value backups at specific belief points, rather than over the entire belief simplex. The efficiency of this approach, however, depends greatly on the selection of points. This paper presents a set of novel techniques for selecting informative belief points which work well in practice. The point selection procedure is combined with point-based value backups to form an effective anytime POMDP algorithm called Point-Based Value Iteration (PBVI). The first aim of this paper is to introduce this algorithm and present a theoretical analysis justifying the choice of belief selection technique. The second aim of this paper is to provide a thorough empirical comparison between PBVI and other state-of-the-art POMDP methods, in particular the Perseus algorithm, in an effort to highlight their similarities and differences. Evaluation is performed using both standard POMDP domains and realistic robotic tasks.
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In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand, as is often the case. We propose a non-linear scalarization function, called the Chebyshev scalarization function, as a basis for action selection strategies in multi-objective reinforcement learning. The Chebyshev scalarization method overcomes the flaws of the linear scalarization function as it can (i) discover Pareto optimal solutions regardless of the shape of the front, i.e. convex as well as non-convex , (ii) obtain a better spread amongst the set of Pareto optimal solutions and (iii) is not particularly dependent on the actual weights used.
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机器学习已成为我们日常生活中许多方面的重要组成部分。然而,构建表现良好的机器学习应用程序需要高度专业化的数据科学家和领域专家。自动机器学习(AutoML)旨在通过使domainexperts能够自动构建机器学习应用程序而无需广泛了解统计数据和机器学习,从而减少对数据科学家的需求。在本次调查中,我们总结了学术界和工业界对AutoML的最新发展。首先,我们介绍一个整体问题的表述。接下来,介绍了解决AutoML变量问题的方法。最后,我们对所提出的合成和实际数据方法进行了广泛的经验评估。
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