首页 > 最新文献

Journal of Heuristics最新文献

英文 中文
Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms 遗传编程超启发式进化风电场维护政策
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-29 DOI: 10.1007/s10732-024-09533-2
Yikai Ma, Wenjuan Zhang, Juergen Branke

Reducing the cost of operating and maintaining wind farms is essential for the economic viability of this renewable energy source. This study applies hyper-heuristics to design a maintenance policy that prescribes the best maintenance action in every possible situation. Genetic programming is used to construct a priority function that determines what maintenance activities to conduct and the sequence of maintenance activities if there are not enough resources to do all of them simultaneously. The priority function may take into account the health condition of the target turbine and its components, the characteristics of the corresponding maintenance work, the workload of the maintenance crew, the working condition of the whole wind farm and the possibilities provided by opportunistic maintenance. Empirical results using a simulation model of the wind farm demonstrate that the proposed model can construct maintenance policies that perform well both in training and test scenarios, which shows the practicability of the approach.

降低风力发电场的运营和维护成本对这种可再生能源的经济可行性至关重要。本研究采用超启发式设计维护政策,在各种可能的情况下规定最佳维护行动。遗传编程被用来构建一个优先级函数,以确定进行哪些维护活动,以及在没有足够资源同时进行所有维护活动的情况下维护活动的顺序。优先级函数可考虑目标风机及其部件的健康状况、相应维护工作的特点、维护人员的工作量、整个风电场的工作状况以及机会维护提供的可能性。使用风电场仿真模型得出的经验结果表明,所提出的模型可以构建在培训和测试场景中均表现良好的维护策略,这表明了该方法的实用性。
{"title":"Genetic programming hyper-heuristic for evolving a maintenance policy for wind farms","authors":"Yikai Ma, Wenjuan Zhang, Juergen Branke","doi":"10.1007/s10732-024-09533-2","DOIUrl":"https://doi.org/10.1007/s10732-024-09533-2","url":null,"abstract":"<p>Reducing the cost of operating and maintaining wind farms is essential for the economic viability of this renewable energy source. This study applies hyper-heuristics to design a maintenance policy that prescribes the best maintenance action in every possible situation. Genetic programming is used to construct a priority function that determines what maintenance activities to conduct and the sequence of maintenance activities if there are not enough resources to do all of them simultaneously. The priority function may take into account the health condition of the target turbine and its components, the characteristics of the corresponding maintenance work, the workload of the maintenance crew, the working condition of the whole wind farm and the possibilities provided by opportunistic maintenance. Empirical results using a simulation model of the wind farm demonstrate that the proposed model can construct maintenance policies that perform well both in training and test scenarios, which shows the practicability of the approach.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"31 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An integrated ILS-VND strategy for solving the knapsack problem with forfeits 解决有弃权的 "knapsack "问题的综合 ILS-VND 策略
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-06 DOI: 10.1007/s10732-024-09532-3
Matheus M. Vieira, Bruno Nogueira, Rian G. S. Pinheiro

This work address a variant of the knapsack problem, known as the knapsack problem with forfeits, which has numerous applications. In this variant, a set of items and a conflict graph are given, and the objective is to identify a collection of items that adhere to the knapsack’s capacity while maximizing the total value of the items minus the penalties for conflicting items. We propose a novel heuristic for this problem based on the concepts of iterated local search, variable neighborhood descent, and tabu search. Our heuristic takes into account four neighborhood structures, and we introduce efficient data structures to explore them. Experimental results demonstrate that our approach outperforms the state-of-the-art algorithms in the literature. In particular, it delivers superior solutions within significantly shorter computation times across all benchmark instances. Additionally, this study includes an analysis of how the proposed data structures have influenced both the quality of the solutions and the execution time of the method.

这项研究针对的是knapsack问题的一个变体,即有弃权的knapsack问题,该问题应用广泛。在这一变体中,给定了一组物品和一个冲突图,目标是找出一个物品集合,既要符合背包的容量,又要最大化物品的总价值减去冲突物品的惩罚。我们根据迭代局部搜索、可变邻域下降和塔布搜索的概念,为这一问题提出了一种新颖的启发式方法。我们的启发式考虑了四种邻域结构,并引入了有效的数据结构来探索它们。实验结果表明,我们的方法优于文献中最先进的算法。特别是,在所有基准实例中,它都能在显著缩短的计算时间内提供出色的解决方案。此外,本研究还分析了所提出的数据结构如何影响解的质量和方法的执行时间。
{"title":"An integrated ILS-VND strategy for solving the knapsack problem with forfeits","authors":"Matheus M. Vieira, Bruno Nogueira, Rian G. S. Pinheiro","doi":"10.1007/s10732-024-09532-3","DOIUrl":"https://doi.org/10.1007/s10732-024-09532-3","url":null,"abstract":"<p>This work address a variant of the knapsack problem, known as the knapsack problem with forfeits, which has numerous applications. In this variant, a set of items and a conflict graph are given, and the objective is to identify a collection of items that adhere to the knapsack’s capacity while maximizing the total value of the items minus the penalties for conflicting items. We propose a novel heuristic for this problem based on the concepts of iterated local search, variable neighborhood descent, and tabu search. Our heuristic takes into account four neighborhood structures, and we introduce efficient data structures to explore them. Experimental results demonstrate that our approach outperforms the state-of-the-art algorithms in the literature. In particular, it delivers superior solutions within significantly shorter computation times across all benchmark instances. Additionally, this study includes an analysis of how the proposed data structures have influenced both the quality of the solutions and the execution time of the method.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"4 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On the emerging potential of quantum annealing hardware for combinatorial optimization 量子退火硬件在组合优化方面的新兴潜力
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-02 DOI: 10.1007/s10732-024-09530-5
Byron Tasseff, Tameem Albash, Zachary Morrell, Marc Vuffray, Andrey Y. Lokhov, Sidhant Misra, Carleton Coffrin

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’ Advantage Performance Update computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work does not present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future.

在过去十年中,量子退火硬件在组合优化中的实用性一直是争论的焦点。迄今为止,实验基准研究表明,量子退火硬件与最先进的优化方法相比,并没有带来无可辩驳的性能提升。然而,随着量子退火硬件的不断发展,每一次新的迭代都会带来性能的提升,因此有必要进行进一步的基准测试。为此,本研究对 D-Wave 系统公司的优势性能更新计算机进行了优化性能评估,该计算机可以解决超过 5000 个二元决策变量和 40000 个二次项的稀疏无约束二次优化问题。我们证明,与一系列成熟的经典求解方法相比,量子退火器可以在运行时间内解决某些特定问题,而这些方法代表了当前量子退火硬件的最先进水平。虽然这项工作并没有提供有力证据证明这种新兴优化技术具有无可辩驳的性能优势,但它确实展示了令人鼓舞的进展,预示着未来对实际优化任务的潜在影响。
{"title":"On the emerging potential of quantum annealing hardware for combinatorial optimization","authors":"Byron Tasseff, Tameem Albash, Zachary Morrell, Marc Vuffray, Andrey Y. Lokhov, Sidhant Misra, Carleton Coffrin","doi":"10.1007/s10732-024-09530-5","DOIUrl":"https://doi.org/10.1007/s10732-024-09530-5","url":null,"abstract":"<p>Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems’ <i>Advantage Performance Update</i> computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work <i>does not</i> present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141885709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles 异构车辆灾后连接问题的 MILP 模型和启发式算法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-18 DOI: 10.1007/s10732-024-09531-4
İlknur Tükenmez, Tugba Saraç, Onur Kaya

Throughout the response phase of the disaster, the speedy restoration of transportation by reconnecting the nodes where the connection is broken is absolutely critical for evacuating civilians, providing clear access to hospitals, and distributing aid. Following a disaster, some roads in a disaster area might be closed to transportation. In reality, some roads can be blocked due to debris, and some of roads can be blocked by collapsing. In this model, different types of road unblocking methods are included, and each road can only be opened to access by a vehicle suitable for that method. So, different types of vehicles may be needed to repair the roads depending on the type of damage. In addition, fast-built bridges built both on land and over water are also used if necessary following a disaster. In problems of this nature, it is essential to restore the roads to enable the complete connectivity of the network such that all nodes can be reached by one another. In addition, it is also critical for the speedy reach of critical nodes, such as hospitals, and emergency disaster centers. This study aims to reduce the maximum time for connection and minimize the total time in which to reach critical nodes. For this purpose, we developed a bi-objective mathematical model that considers the multiple vehicle types that can repair different types of damages. Since the problem is NP-hard, two heuristic methods were developed, and the numerical results were presented. It has been observed that the local search algorithm gives better results than the hybrid algorithm. Additionally, different scenario data was produced. Numbers of unconnected components from 3 to 10 are solved with heuristic algorithms for test data containing 80 and 250 nodes, and real-life data containing 223 nodes and 391 edges are solved with heuristic algorithms for the number of unconnected components 6, 9, 12, and 15.

在整个灾难应对阶段,通过重新连接中断的节点来迅速恢复交通,对于疏散平民、提供通往医院的畅通通道以及分发援助物资绝对至关重要。灾难发生后,灾区的一些道路可能会关闭,无法进行运输。在现实中,一些道路可能因瓦砾而堵塞,一些道路可能因坍塌而堵塞。在这个模型中,包括了不同类型的道路疏通方法,每条道路只能由适合该方法的车辆开放通行。因此,根据损坏类型的不同,可能需要不同类型的车辆来修复道路。此外,如有必要,还可在灾后使用在陆地和水上修建的快速桥梁。在这种性质的问题中,必须恢复道路,以实现网络的完整连接,使所有节点都能相互到达。此外,快速到达关键节点(如医院和紧急救灾中心)也至关重要。本研究旨在缩短连接的最长时间,并最大限度地减少到达关键节点的总时间。为此,我们开发了一个双目标数学模型,该模型考虑了可修复不同类型损坏的多种车辆类型。由于该问题具有 NP 难度,我们开发了两种启发式方法,并给出了数值结果。据观察,局部搜索算法比混合算法结果更好。此外,还生成了不同的场景数据。在包含 80 和 250 个节点的测试数据中,使用启发式算法解决了 3 至 10 个无连接部件的问题;在包含 223 个节点和 391 条边的实际数据中,使用启发式算法解决了 6、9、12 和 15 个无连接部件的问题。
{"title":"A MILP model and a heuristic algorithm for post-disaster connectivity problem with heterogeneous vehicles","authors":"İlknur Tükenmez, Tugba Saraç, Onur Kaya","doi":"10.1007/s10732-024-09531-4","DOIUrl":"https://doi.org/10.1007/s10732-024-09531-4","url":null,"abstract":"<p>Throughout the response phase of the disaster, the speedy restoration of transportation by reconnecting the nodes where the connection is broken is absolutely critical for evacuating civilians, providing clear access to hospitals, and distributing aid. Following a disaster, some roads in a disaster area might be closed to transportation. In reality, some roads can be blocked due to debris, and some of roads can be blocked by collapsing. In this model, different types of road unblocking methods are included, and each road can only be opened to access by a vehicle suitable for that method. So, different types of vehicles may be needed to repair the roads depending on the type of damage. In addition, fast-built bridges built both on land and over water are also used if necessary following a disaster. In problems of this nature, it is essential to restore the roads to enable the complete connectivity of the network such that all nodes can be reached by one another. In addition, it is also critical for the speedy reach of critical nodes, such as hospitals, and emergency disaster centers. This study aims to reduce the maximum time for connection and minimize the total time in which to reach critical nodes. For this purpose, we developed a bi-objective mathematical model that considers the multiple vehicle types that can repair different types of damages. Since the problem is NP-hard, two heuristic methods were developed, and the numerical results were presented. It has been observed that the local search algorithm gives better results than the hybrid algorithm. Additionally, different scenario data was produced. Numbers of unconnected components from 3 to 10 are solved with heuristic algorithms for test data containing 80 and 250 nodes, and real-life data containing 223 nodes and 391 edges are solved with heuristic algorithms for the number of unconnected components 6, 9, 12, and 15.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"30 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Assignment of orthologous genes in unbalanced genomes using cycle packing of adjacency graphs 利用邻接图的循环包装分配不平衡基因组中的同源基因
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-31 DOI: 10.1007/s10732-024-09528-z
Gabriel Siqueira, Andre Rodrigues Oliveira, Alexsandro Oliveira Alexandrino, Géraldine Jean, Guillaume Fertin, Zanoni Dias

The adjacency graph is a structure used to model genomes in several rearrangement distance problems. In particular, most studies use properties of a maximum cycle packing of this graph to develop bounds and algorithms for rearrangement distance problems, such as the reversal distance, the reversal and transposition distance, and the double cut and join distance. When each genome has no repeated genes, there exists only one cycle packing for the graph. However, when each genome may have repeated genes, the problem of finding a maximum cycle packing for the adjacency graph (adjacency graph packing) is NP-hard. In this work, we develop a randomized greedy heuristic and a genetic algorithm heuristic for the adjacency graph packing problem for genomes with repeated genes and unequal gene content. We also propose new algorithms with simple implementation and good practical performance for reversal distance and reversal and transposition distance in genomes without repeated genes, which we combine with the heuristics to find solutions for the problems with repeated genes. We present experimental results and compare the application of these heuristics with the application of the MSOAR framework in rearrangement distance problems. Lastly, we apply our genetic algorithm heuristic to real genomic data to validate its practical use.

邻接图是几种重排距离问题中用来模拟基因组的结构。特别是,大多数研究利用该图的最大循环包装的特性来开发重排距离问题的边界和算法,如反转距离、反转和换位距离以及双切和连接距离。当每个基因组没有重复基因时,该图只存在一个循环包装。然而,当每个基因组可能有重复基因时,为邻接图寻找最大循环包装(邻接图包装)的问题是 NP-困难的。在这项研究中,我们开发了一种随机贪婪启发式和一种遗传算法启发式,用于解决具有重复基因和不等基因含量的基因组的邻接图打包问题。我们还针对无重复基因的基因组中的反转距离和反转与换位距离提出了实现简单、实用性能良好的新算法,并将其与启发式算法相结合,为有重复基因的问题找到了解决方案。我们展示了实验结果,并比较了这些启发式方法与 MSOAR 框架在重排距离问题中的应用。最后,我们将我们的遗传算法启发式应用于真实的基因组数据,以验证其实际用途。
{"title":"Assignment of orthologous genes in unbalanced genomes using cycle packing of adjacency graphs","authors":"Gabriel Siqueira, Andre Rodrigues Oliveira, Alexsandro Oliveira Alexandrino, Géraldine Jean, Guillaume Fertin, Zanoni Dias","doi":"10.1007/s10732-024-09528-z","DOIUrl":"https://doi.org/10.1007/s10732-024-09528-z","url":null,"abstract":"<p>The adjacency graph is a structure used to model genomes in several rearrangement distance problems. In particular, most studies use properties of a maximum cycle packing of this graph to develop bounds and algorithms for rearrangement distance problems, such as the reversal distance, the reversal and transposition distance, and the double cut and join distance. When each genome has no repeated genes, there exists only one cycle packing for the graph. However, when each genome may have repeated genes, the problem of finding a maximum cycle packing for the adjacency graph (adjacency graph packing) is NP-hard. In this work, we develop a randomized greedy heuristic and a genetic algorithm heuristic for the adjacency graph packing problem for genomes with repeated genes and unequal gene content. We also propose new algorithms with simple implementation and good practical performance for reversal distance and reversal and transposition distance in genomes without repeated genes, which we combine with the heuristics to find solutions for the problems with repeated genes. We present experimental results and compare the application of these heuristics with the application of the MSOAR framework in rearrangement distance problems. Lastly, we apply our genetic algorithm heuristic to real genomic data to validate its practical use.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"229 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mathematical models and solving methods for diversity and equity optimization 多样性和公平性优化的数学模型和求解方法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-05-30 DOI: 10.1007/s10732-024-09529-y
Rafael Martí, Francisco Parreño, Jorge Mortes

Discrete diversity optimization basically consists of selecting a subset of elements of a given set in such a way that the sum of their pairwise distances is maximized. Equity, on the other hand, refers to minimizing the difference between the maximum and the minimum distances in the subset of selected elements to balance their diversity. Both problems have been studied in the combinatorial optimization literature, but recently major drawbacks in their classic mathematical formulations have been identified. We propose new mathematical models to overcome these limitations, including multi-objective optimization, and heuristics to solve large-size instances of them. Specifically, we propose a matheuristic based on the CMSA framework for diversity and a GRASP heuristic for equity. Our extensive experimentation compares the original models with the new proposals by analyzing the solutions of our heuristics and those of the previous approaches, both from a single objective and a bi-objective paradigm. We also evaluate their quality with respect to the optimal solutions obtained with CPLEX, size permitting. Statistical analysis allows us to draw significant conclusions.

离散多样性优化的基本原理是,从给定集合中选择一个元素子集,使它们的成对距离之和达到最大。另一方面,均衡是指最小化所选元素子集中最大距离和最小距离之间的差值,以平衡其多样性。这两个问题在组合优化文献中都有研究,但最近发现它们的经典数学公式存在重大缺陷。我们提出了新的数学模型来克服这些局限性,包括多目标优化,以及解决其大型实例的启发式方法。具体来说,我们提出了一种基于 CMSA 框架的多样性数学启发式和一种公平性 GRASP 启发式。我们进行了广泛的实验,从单一目标和双目标范例出发,通过分析我们的启发式方法和之前方法的解决方案,对原始模型和新建议进行了比较。在规模允许的情况下,我们还根据 CPLEX 获得的最优解对它们的质量进行了评估。通过统计分析,我们得出了重要结论。
{"title":"Mathematical models and solving methods for diversity and equity optimization","authors":"Rafael Martí, Francisco Parreño, Jorge Mortes","doi":"10.1007/s10732-024-09529-y","DOIUrl":"https://doi.org/10.1007/s10732-024-09529-y","url":null,"abstract":"<p>Discrete diversity optimization basically consists of selecting a subset of elements of a given set in such a way that the sum of their pairwise distances is maximized. Equity, on the other hand, refers to minimizing the difference between the maximum and the minimum distances in the subset of selected elements to balance their diversity. Both problems have been studied in the combinatorial optimization literature, but recently major drawbacks in their classic mathematical formulations have been identified. We propose new mathematical models to overcome these limitations, including multi-objective optimization, and heuristics to solve large-size instances of them. Specifically, we propose a matheuristic based on the CMSA framework for diversity and a GRASP heuristic for equity. Our extensive experimentation compares the original models with the new proposals by analyzing the solutions of our heuristics and those of the previous approaches, both from a single objective and a bi-objective paradigm. We also evaluate their quality with respect to the optimal solutions obtained with CPLEX, size permitting. Statistical analysis allows us to draw significant conclusions.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"293 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search 元合方法与机器学习:强化学习辅助神经结构搜索的方法
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-04-16 DOI: 10.1007/s10732-024-09526-1
Sandra Mara Scós Venske, Carolina Paula de Almeida , Myriam Regattieri Delgado

Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS(_{in})EA(_{in})ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS(_{in})EA(_{in})ANN performs significantly better than a canonical genetic algorithm (GA(_{in})ANN) and the evolutionary algorithm without reinforcement learning (EA(_{in})ANN). Analyses of the parameter’s frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS(_{in})EA(_{in})ANN outperforms other approaches considered the state of the art for the addressed datasets.

元启发式(MH)是一种广泛用于解决复杂优化问题的技术。近年来,人们对 MH 与机器学习(ML)的结合越来越感兴趣。这种结合主要有两种方式:ML-in-MH和MH-in-ML。在本研究中,我们将这两种方式中的技术结合起来--ML-in-MH-in-ML,提供了一种方法,即考虑用 ML 来提高进化算法(EA)的性能,而进化算法的解决方案编码了 ML 模型--人工神经网络(ANN)的参数。我们的方法称为 TS (_{in})EA (_{in})ANN,它采用了基于汤普森采样(Thompson sampling,TS)的强化学习邻域(RLN)突变。TS是一种无参数强化学习方法,在此用于提高EA性能。在实验中,每个候选 ANN 都要解决一个回归问题,即蛋白质结构预测偏差。我们考虑了两个蛋白质数据集,一个有 16,382 个样本,另一个有 45,730 个样本。结果表明,TS/(_{in}/)EA/(_{in}/)ANN的性能明显优于典型遗传算法(GA/(_{in}/)ANN)和无强化学习的进化算法(EA/(_{in}/)ANN)。此外,还对各种方法的参数频率进行了分析比较。最后,与文献的比较表明,除了最大数据集中的一个特殊情况外,TS/(_{in})EA/(_{in})ANN 在所处理的数据集上优于被认为是最先进的其他方法。
{"title":"Metaheuristics and machine learning: an approach with reinforcement learning assisting neural architecture search","authors":"Sandra Mara Scós Venske, Carolina Paula de Almeida , Myriam Regattieri Delgado","doi":"10.1007/s10732-024-09526-1","DOIUrl":"https://doi.org/10.1007/s10732-024-09526-1","url":null,"abstract":"<p>Methaheuristics (MHs) are techniques widely used for solving complex optimization problems. In recent years, the interest in combining MH and machine learning (ML) has grown. This integration can occur mainly in two ways: ML-in-MH and MH-in-ML. In the present work, we combine the techniques in both ways—ML-in-MH-in-ML, providing an approach in which ML is considered to improve the performance of an evolutionary algorithm (EA), whose solutions encode parameters of an ML model—artificial neural network (ANN). Our approach called TS<span>(_{in})</span>EA<span>(_{in})</span>ANN employs a reinforcement learning neighborhood (RLN) mutation based on Thompson sampling (TS). TS is a parameterless reinforcement learning method, used here to boost the EA performance. In the experiments, every candidate ANN solves a regression problem known as protein structure prediction deviation. We consider two protein datasets, one with 16,382 and the other with 45,730 samples. The results show that TS<span>(_{in})</span>EA<span>(_{in})</span>ANN performs significantly better than a canonical genetic algorithm (GA<span>(_{in})</span>ANN) and the evolutionary algorithm without reinforcement learning (EA<span>(_{in})</span>ANN). Analyses of the parameter’s frequency are also performed comparing the approaches. Finally, comparisons with the literature show that except for one particular case in the largest dataset, TS<span>(_{in})</span>EA<span>(_{in})</span>ANN outperforms other approaches considered the state of the art for the addressed datasets.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"25 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140594981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Interpretability of rectangle packing solutions with Monte Carlo tree search 用蒙特卡洛树搜索矩形包装解决方案的可解释性
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-03-18 DOI: 10.1007/s10732-024-09525-2
Yeray Galán López, Cristian González García, Vicente García Díaz, Edward Rolando Núñez Valdez, Alberto Gómez Gómez

Packing problems have been studied for a long time and have great applications in real-world scenarios. In recent times, with problems in the industrial world increasing in size, exact algorithms are often not a viable option and faster approaches are needed. We study Monte Carlo tree search, a random sampling algorithm that has gained great importance in literature in the last few years. We propose three approaches based on MCTS and its integration with metaheuristic algorithms or deep learning models to obtain approximated solutions to packing problems that are also interpretable by means of MCTS exploration and from which knowledge can be extracted. We focus on two-dimensional rectangle packing problems in our experimentation and use several well known benchmarks from literature to compare our solutions with existing approaches and offer a view on the potential uses for knowledge extraction from our method. We manage to match the quality of state-of-the-art methods, with improvements in time with respect to some of them and greater interpretability.

打包问题的研究由来已久,在现实世界中有着广泛的应用。近来,随着工业领域问题规模的不断扩大,精确算法往往不可行,需要更快的方法。我们研究的蒙特卡洛树搜索是一种随机抽样算法,在过去几年的文献中获得了极大的重视。我们提出了三种基于蒙特卡洛树搜索的方法,并将其与元启发式算法或深度学习模型相结合,以获得包装问题的近似解,这些解也可通过蒙特卡洛树搜索进行解释,并从中提取知识。我们在实验中重点关注二维矩形打包问题,并使用文献中几个众所周知的基准,将我们的解决方案与现有方法进行比较,并就从我们的方法中提取知识的潜在用途提出看法。我们的方法在质量上与最先进的方法不相上下,在时间上也比其中一些方法有所改进,并且具有更强的可解释性。
{"title":"Interpretability of rectangle packing solutions with Monte Carlo tree search","authors":"Yeray Galán López, Cristian González García, Vicente García Díaz, Edward Rolando Núñez Valdez, Alberto Gómez Gómez","doi":"10.1007/s10732-024-09525-2","DOIUrl":"https://doi.org/10.1007/s10732-024-09525-2","url":null,"abstract":"<p>Packing problems have been studied for a long time and have great applications in real-world scenarios. In recent times, with problems in the industrial world increasing in size, exact algorithms are often not a viable option and faster approaches are needed. We study Monte Carlo tree search, a random sampling algorithm that has gained great importance in literature in the last few years. We propose three approaches based on MCTS and its integration with metaheuristic algorithms or deep learning models to obtain approximated solutions to packing problems that are also interpretable by means of MCTS exploration and from which knowledge can be extracted. We focus on two-dimensional rectangle packing problems in our experimentation and use several well known benchmarks from literature to compare our solutions with existing approaches and offer a view on the potential uses for knowledge extraction from our method. We manage to match the quality of state-of-the-art methods, with improvements in time with respect to some of them and greater interpretability.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"21 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140149864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A hybrid iterated local search matheuristic for large-scale single source capacitated facility location problems 大规模单源容纳式设施选址问题的混合迭代局部搜索数学启发式
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-12-26 DOI: 10.1007/s10732-023-09524-9
Guilherme Barbosa de Almeida, Elisangela Martins de Sá, Sérgio Ricardo de Souza, Marcone Jamilson Freitas Souza

The Single Source Capacitated Facility Location Problem (SSCFLP) consists of determining locations for facilities to meet customer demands so that each customer must be served by a single facility. This paper proposes a matheuristic algorithm for solving large-scale SSCFLP instances that combines neighborhood-based heuristic procedures with the solution of two binary linear programming sub-problems through a general-purpose solver. The proposed algorithm starts from the optimal solution of the linear relaxation of the SSCFLP to reduce its size and identify promising potential locations for opening facilities. Computational experiments were performed on two benchmark sets of large instances. For one of them, the developed algorithm obtained optimal solutions for all instances. For the other set, it provided average relative deviations slightly lower than those of three relevant algorithms from the literature. These results allow us to conclude that the proposed algorithm generates good-quality solutions and is competitive in solving large-scale SSCFLP instances.

单源产能设施选址问题(SSCFLP)包括确定设施选址以满足客户需求,从而使每个客户必须由单一设施提供服务。本文提出了一种用于求解大规模 SSCFLP 实例的数学启发式算法,该算法将基于邻域的启发式程序与通过通用求解器求解两个二元线性规划子问题相结合。所提出的算法从 SSCFLP 线性松弛的最优解出发,以缩小 SSCFLP 的规模,并确定有潜力的潜在开放设施地点。在两个大型实例基准集上进行了计算实验。对于其中一组,所开发的算法获得了所有实例的最优解。对于另一组实例,该算法提供的平均相对偏差略低于文献中的三种相关算法。这些结果让我们得出结论,所提出的算法能生成高质量的解,在解决大规模 SSCFLP 实例方面具有竞争力。
{"title":"A hybrid iterated local search matheuristic for large-scale single source capacitated facility location problems","authors":"Guilherme Barbosa de Almeida, Elisangela Martins de Sá, Sérgio Ricardo de Souza, Marcone Jamilson Freitas Souza","doi":"10.1007/s10732-023-09524-9","DOIUrl":"https://doi.org/10.1007/s10732-023-09524-9","url":null,"abstract":"<p>The Single Source Capacitated Facility Location Problem (SSCFLP) consists of determining locations for facilities to meet customer demands so that each customer must be served by a single facility. This paper proposes a matheuristic algorithm for solving large-scale SSCFLP instances that combines neighborhood-based heuristic procedures with the solution of two binary linear programming sub-problems through a general-purpose solver. The proposed algorithm starts from the optimal solution of the linear relaxation of the SSCFLP to reduce its size and identify promising potential locations for opening facilities. Computational experiments were performed on two benchmark sets of large instances. For one of them, the developed algorithm obtained optimal solutions for all instances. For the other set, it provided average relative deviations slightly lower than those of three relevant algorithms from the literature. These results allow us to conclude that the proposed algorithm generates good-quality solutions and is competitive in solving large-scale SSCFLP instances.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"44 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139055871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Algorithmic strategies for a fast exploration of the TSP $$4$$ -OPT neighborhood 快速探索 TSP $$4$$ -OPT 邻域的算法策略
IF 2.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-12-21 DOI: 10.1007/s10732-023-09523-w
Giuseppe Lancia, Marcello Dalpasso

We describe an effective algorithm for exploring the (4)-OPT neighborhood for the Traveling Salesman Problem. (4)-OPT moves change a tour into another by replacing four of its edges. The best move can be found by a (Theta (n^4)) algorithm by complete enumeration, but a (Theta (n^3)) dynamic programming algorithm exists in the literature. Furthermore a (Theta (n^2)) algorithm also exists for a particular subset of symmetric (4)-OPT moves. In this work we describe a new procedure which behaves, on average, slightly worse than a quadratic algorithm over all moves (estimated at (O(n^{2.5}))) and like a quadratic algorithm on the symmetric moves. Computational results are reported which show the effectiveness of our strategy compared to other algorithms for finding the best (4)-OPT move, and discuss the strength of the (4)-OPT neighborhood compared to 2- and (3)-OPT.

我们描述了一种探索旅行推销员问题的 (4)-OPT 邻域的有效算法。(4)-OPT 移动通过替换四条边将一个旅行变为另一个旅行。最佳移动可以通过完全枚举的算法找到,但是文献中存在一种动态编程算法。此外,对于一个特定的对称(4)-OPT移动子集,也存在一个(Theta (n^2))算法。在这项工作中,我们描述了一种新的程序,平均而言,它在所有棋步上的表现略差于二次算法(估计为 (O(n^{2.5})),而在对称棋步上则类似于二次算法。报告中的计算结果显示了我们的策略与其他算法相比在寻找最佳 (4)-OPT 移动方面的有效性,并讨论了 (4)-OPT 邻域与 2- 和 (3)-OPT 相比的优势。
{"title":"Algorithmic strategies for a fast exploration of the TSP $$4$$ -OPT neighborhood","authors":"Giuseppe Lancia, Marcello Dalpasso","doi":"10.1007/s10732-023-09523-w","DOIUrl":"https://doi.org/10.1007/s10732-023-09523-w","url":null,"abstract":"<p>We describe an effective algorithm for exploring the <span>(4)</span>-OPT neighborhood for the Traveling Salesman Problem. <span>(4)</span>-OPT moves change a tour into another by replacing four of its edges. The best move can be found by a <span>(Theta (n^4))</span> algorithm by complete enumeration, but a <span>(Theta (n^3))</span> dynamic programming algorithm exists in the literature. Furthermore a <span>(Theta (n^2))</span> algorithm also exists for a particular subset of symmetric <span>(4)</span>-OPT moves. In this work we describe a new procedure which behaves, on average, slightly worse than a quadratic algorithm over all moves (estimated at <span>(O(n^{2.5}))</span>) and like a quadratic algorithm on the symmetric moves. Computational results are reported which show the effectiveness of our strategy compared to other algorithms for finding the best <span>(4)</span>-OPT move, and discuss the strength of the <span>(4)</span>-OPT neighborhood compared to 2- and <span>(3)</span>-OPT.</p>","PeriodicalId":54810,"journal":{"name":"Journal of Heuristics","volume":"20 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Journal of Heuristics
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1