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Journal of Heuristics最新文献

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A heuristic algorithm using tree decompositions for the maximum happy vertices problem 利用树分解的启发式算法求解最大快乐顶点问题
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-11-15 DOI: 10.1007/s10732-023-09522-x
Louis Carpentier, Jorik Jooken, Jan Goedgebeur

We propose a new methodology to develop heuristic algorithms using tree decompositions. Traditionally, such algorithms construct an optimal solution of the given problem instance through a dynamic programming approach. We modify this procedure by introducing a parameter W that dictates the number of dynamic programming states to consider. We drop the exactness guarantee in favour of a shorter running time. However, if W is large enough such that all valid states are considered, our heuristic algorithm proves optimality of the constructed solution. In particular, we implement a heuristic algorithm for the Maximum Happy Vertices problem using this approach. Our algorithm more efficiently constructs optimal solutions compared to the exact algorithm for graphs of bounded treewidth. Furthermore, our algorithm constructs higher quality solutions than state-of-the-art heuristic algorithms Greedy-MHV and Growth-MHV for instances of which at least 40% of the vertices are initially coloured, at the cost of a larger running time.

我们提出了一种使用树分解开发启发式算法的新方法。传统上,这类算法通过动态规划方法构造给定问题实例的最优解。我们通过引入一个参数W来修改这个过程,该参数W决定了要考虑的动态规划状态的数量。为了缩短运行时间,我们放弃了准确性保证。然而,如果W足够大,以至于考虑了所有有效状态,我们的启发式算法证明了构造解的最优性。特别地,我们使用这种方法实现了一个启发式算法来解决最大快乐顶点问题。与有界树宽图的精确算法相比,我们的算法更有效地构建了最优解。此外,我们的算法比最先进的启发式算法Greedy-MHV和Growth-MHV构建了更高质量的解决方案,其中至少40%的顶点最初是着色的,但代价是更长的运行时间。
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引用次数: 0
DSLS: a simple and efficient local search algorithm for the maximum bisection problem DSLS:一个简单有效的局部搜索算法的最大平分问题
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-11-13 DOI: 10.1007/s10732-023-09521-y
Xinliang Tian, Dantong Ouyang, Huisi Zhou, Rui Sun, Liming Zhang
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引用次数: 0
A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations 一种混合变量邻域搜索启发式方法用于具有集合地点的可持续时变卡车-无人机路径问题
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-11-11 DOI: 10.1007/s10732-023-09520-z
Ebrahim Teimoury, Reza Rashid
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引用次数: 0
Effective adaptive large neighborhood search for a firefighters timetabling problem 消防员调度问题的有效自适应大邻域搜索
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-10-13 DOI: 10.1007/s10732-023-09519-6
Mohamed-Amine Ouberkouk, Jean-Paul Boufflet, Aziz Moukrim
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引用次数: 0
Solving travelling thief problems using coordination based methods 利用基于协调的方法解决行窃问题
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-10-09 DOI: 10.1007/s10732-023-09518-7
Majid Namazi, M. A. Hakim Newton, Conrad Sanderson, Abdul Sattar
A travelling thief problem (TTP) is a proxy to real-life problems such as postal collection. TTP comprises an entanglement of a travelling salesman problem (TSP) and a knapsack problem (KP) since items of KP are scattered over cities of TSP, and a thief has to visit cities to collect items. In TTP, city selection and item selection decisions need close coordination since the thief's travelling speed depends on the knapsack's weight and the order of visiting cities affects the order of item collection. Existing TTP solvers deal with city selection and item selection separately, keeping decisions for one type unchanged while dealing with the other type. This separation essentially means very poor coordination between two types of decision. In this paper, we first show that a simple local search based coordination approach does not work in TTP. Then, to address the aforementioned problems, we propose a human designed coordination heuristic that makes changes to collection plans during exploration of cyclic tours. We further propose another human designed coordination heuristic that explicitly exploits the cyclic tours in item selections during collection plan exploration. Lastly, we propose a machine learning based coordination heuristic that captures characteristics of the two human designed coordination heuristics. Our proposed coordination based approaches help our TTP solver significantly outperform existing state-of-the-art TTP solvers on a set of benchmark problems. Our solver is named Cooperation Coordination (CoCo) and its source code is available from https://github.com/majid75/CoCo
旅行小偷问题(TTP)是现实生活中的问题,如邮政收集的代理。TTP包括一个旅行推销员问题(TSP)和一个背包问题(KP)的纠缠,因为KP的物品分散在TSP的城市,小偷必须访问城市收集物品。在TTP中,城市选择和物品选择决策需要密切协调,因为小偷的行进速度取决于背包的重量,而访问城市的顺序会影响物品收集的顺序。现有的TTP求解器分别处理城市选择和物品选择,在处理另一种类型时保持一种类型的决策不变。这种分离本质上意味着两种决策之间的协调非常差。在本文中,我们首先证明了一个简单的基于局部搜索的协调方法并不适用于TTP。然后,为了解决上述问题,我们提出了一种人类设计的协调启发式方法,该方法可以在循环旅行的探索过程中更改收集计划。我们进一步提出了另一种人类设计的协调启发式,该启发式明确地利用了收集计划探索过程中物品选择的循环循环。最后,我们提出了一种基于机器学习的协调启发式,它捕捉了两种人类设计的协调启发式的特征。我们提出的基于协调的方法帮助我们的TTP求解器在一系列基准问题上显著优于现有的最先进的TTP求解器。我们的求解器名为合作协调(CoCo),其源代码可从https://github.com/majid75/CoCo获得
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引用次数: 0
On robust vs fast solving of qualitative constraints 关于定性约束的鲁棒与快速求解
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-23 DOI: 10.1007/s10732-023-09517-8
Jan Wehner, Michael Sioutis, Diedrich Wolter
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引用次数: 2
A matheuristic approach for the family traveling salesman problem 家庭旅行商问题的数学方法
4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-09-22 DOI: 10.1007/s10732-023-09516-9
Abtin Nourmohammadzadeh, Malek Sarhani, Stefan Voß
Abstract In the family traveling salesman problem (FTSP), there is a set of cities which are divided into a number of clusters called families. The salesman has to find a shortest possible tour visiting a specific number of cities from each of the families without any restriction of visiting one family before starting the visit of another one. In this work, the general concept of the Partial OPtimization Metaheuristic Under Special Intensification Conditions is linked with the exact optimization by a classical solver using a mathematical programming formulation for the FTSP to develop a matheuristic. Moreover, a genetic and a simulated annealing algorithm are used as metaheuristics embedded in the approach. The method is examined on a set of benchmark instances and its performance is favorably compared with a state-of-the-art approach from literature. Moreover, a careful analysis of the specific components of the approach is undertaken to provide insights into the impact of their interplay.
在家庭旅行商问题(FTSP)中,存在一组城市,这些城市被划分为许多称为家庭的簇。销售人员必须找到一个最短的行程,从每个家庭中访问特定数量的城市,而不受访问一个家庭之前开始访问另一个家庭的限制。在本文中,将特殊强化条件下的部分优化元启发式的一般概念与经典求解器的精确优化联系起来,利用数学规划公式对FTSP进行数学求解。此外,采用遗传和模拟退火算法作为嵌入该方法的元启发式算法。该方法在一组基准实例上进行了测试,其性能优于文献中最先进的方法。此外,还对该方法的具体组成部分进行了仔细分析,以便深入了解其相互作用的影响。
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引用次数: 0
An integrated learning and progressive hedging matheuristic for stochastic network design problem 随机网络设计问题的综合学习与递进对冲数学
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-08-09 DOI: 10.1007/s10732-023-09515-w
Fatemeh Sarayloo, T. Crainic, W. Rei
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引用次数: 0
An efficient scenario penalization matheuristic for a stochastic scheduling problem 随机调度问题的一种有效的情景惩罚数学方法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-29 DOI: 10.1007/s10732-023-09513-y
Michel Vasquez, Mirsad Buljubasic, S. Hanafi
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引用次数: 0
The ALNS metaheuristic for the transmission maintenance scheduling 传输维护调度的ALNS元启发式算法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-27 DOI: 10.1007/s10732-023-09514-x
David Woller, Jakub Rada, Miroslav Kulich
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引用次数: 0
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Journal of Heuristics
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