Journal of Combinatorial Optimization最新文献

{"title":"Better approximating SONET k-edge partition for small capacity k","authors":"Junhui Ye, Huihuang Jiang, Guangting Chen, Yong Chen, Guohui Lin, An Zhang","doi":"10.1007/s10878-025-01308-0","DOIUrl":"https://doi.org/10.1007/s10878-025-01308-0","url":null,"abstract":"<p>We study the SONET edge partition problem that models telecommunication network design to partition the edge set of a given graph into several edge-disjoint subgraphs, such that each subgraph has size no greater than a given capacity <i>k</i> and the sum of the orders of these subgraphs is minimized. The problem is NP-hard when <span>(k ge 3)</span> and admits an <span>(O(log k))</span>-approximation algorithm. For small capacity <span>(k = 3, 4, 5)</span>, by observing that some subgraph structures are more favorable than the others, we propose modifications to existing algorithms and design novel amortization schemes to prove their improved performance. Our algorithmic results include a <span>(frac{4}{3})</span>-approximation for <span>(k = 3)</span>, improving the previous best <span>(frac{13}{9})</span>-approximation, a <span>(frac{4}{3})</span>-approximation for <span>(k = 4)</span>, improving the previous best <span>((frac{4}{3} + epsilon ))</span>-approximation, and a <span>(frac{3}{2})</span>-approximation for <span>(k = 5)</span>, improving the previous best <span>(frac{5}{3})</span>-approximation. Besides these improved algorithms, our main contribution is the amortization scheme design, which can be helpful for similar algorithms and problems.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"57 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linear-time algorithm for generating L-shaped floorplans using canonical ordering technique","authors":"Shiksha, Krishnendra Shekhawat, Ritu Chandna, Akshaj Gupta","doi":"10.1007/s10878-025-01287-2","DOIUrl":"https://doi.org/10.1007/s10878-025-01287-2","url":null,"abstract":"<p><i>L</i>-shaped floorplans are defined by rectangular modules enclosed within a rectilinear outer boundary, forming an <i>L</i>-shape that can not be altered through simple extension or contraction of a boundary wall. The boundary of such floorplans comprises five convex corners and one concave corner. The concave corner on the boundary of the plan can not be converted into a convex corner without altering the horizontal and vertical adjacency among the modules. This paper introduces a linear-time algorithm based on canonical ordering to generate <i>L</i>-shaped floorplans from properly triangulated plane graphs (PTPGs). Here, modules in the floorplan correspond to the nodes of the given graph, while edges in the graph represent wall adjacency between modules. The proposed algorithm assigns a unique labeling to the given graph, ensuring the presence of a concave corner on the resulting floorplan’s boundary. Simple boundary wall extensions or contractions cannot eliminate this concave corner. It also produces multiple <i>L</i>-shaped floorplans corresponding to the given PTPG, with variations mainly on their concave corners, highlighting the unique configurations possible within the same boundary constraints. Our algorithm offers simplicity over existing methods and is easy to implement. Additionally, we have implemented the algorithm in Python, enabling easy integration for generating <i>L</i>-shaped floorplans in various architectural and VLSI circuit design applications.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143980081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data-driven optimal weighted fused features-based ensemble learning classifier for thyroid prediction with heuristic algorithm","authors":"K. Hema Priya, K. Valarmathi","doi":"10.1007/s10878-025-01304-4","DOIUrl":"https://doi.org/10.1007/s10878-025-01304-4","url":null,"abstract":"<p>Diagnosis of thyroid disease is a most important cause in the field of medicinal research and it is a complex onset axiom. Secretion of Thyroid hormone plays a major role in the regulation of metabolism. Hence, it is very significant to predict thyroid disease in the initial stage, which is helpful for preventing more serious health complications due to thyroid cancer. The diagnostic accuracy of machine leaning-based approaches is greater but these techniques require large amounts of data for the diagnosis process. In the conventional approaches, the time needed for the prediction process is also high. Feature engineering is less investigated in conventional models and hence error produced during the prediction process is high. Hence, in this research work, a machine learning-aided thyroid disease prediction technique is designed to provide higher prediction accuracy and reliability. Initially, the thyroid data is gathered from the standard benchmark resources. Next, the data transformation process is carried out to make the data usable for analysis and visualization. After, the features are extracted using Principal Component Analysis (PCA), “One-Dimensional Convolutional Neural Network Model (1DCNN). Moreover, the statistical features are also extracted for getting more relevant information from the data. The three sets of features such as PCA-based, 1DCNN-based and statistical are concatenated and fed to the “optimal weighted feature selection” process, where the optimal features and weights are tuned by an Improved Archimedes Optimization Algorithm (IAOA). Next, the selected optimally fused features are given to the Ensemble Learning (EL) for predicting the thyroid diseases, where the EL with be suggested by incorporating stacking classifier, XGboost, and Multivariate regression classifier. Ensembling of three different classifiers provides higher thyroid disease prediction accuracy and it makes the decision about normal and abnormal classes. Here, the same IAOA is used for optimizing the parameters of every classifier. The investigational outcomes demonstrate that the proposed ensemble classifier provides higher performance than others. Experimental results prove that the thyroid prediction accuracy of the developed EL approach is 96.30%, precision is 99.67% and F1-score is 97.93%, which is more extensive than the state-of-the-art approaches.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neighbor sum distinguishable $$k$$ -edge colorings of joint graphs","authors":"Xiangzhi Tu, Peng Li, Yangjing Long, Aifa Wang","doi":"10.1007/s10878-025-01309-z","DOIUrl":"https://doi.org/10.1007/s10878-025-01309-z","url":null,"abstract":"<p>In a graph <i>G</i>, the normal <i>k</i>-edge coloring <span>(sigma )</span> is defined as the conventional edge coloring of <i>G</i> using the color set <span>(left[ k right] =left{ 1,2,cdots ,k right} )</span>. If the condition <span>(Sleft( u right) ne Sleft( v right) )</span> holds for any edge <span>(uvin Eleft( G right) )</span>, where <span>(Sleft( u right) =sum nolimits _{uvin Eleft( G right) }{sigma left( uv right) })</span>, then <span>(sigma )</span> is termed a neighbor sum distinguishable <i>k</i>-edge coloring of the graph <i>G</i>, abbreviated as <i>k</i>-VSDEC. The minimum number of colors <span>( k )</span> needed for this type of coloring is referred to as the neighbor sum distinguishable edge chromatic number of <span>( G )</span>, represented as <span>( chi '_{varSigma }(G) )</span>. This paper examines neighbor sum distinguishable <i>k</i>-edge colorings in the joint graphs of an <i>h</i>-order path <span>({{P}_{h}})</span> and an <span>(left( z+1 right) )</span>-order star <span>({{S}_{z}})</span>, providing exact values for their neighboring and distinguishable edge coloring numbers, which are either <span>(varDelta )</span> or <span>(varDelta +1)</span>.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"39 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Randomized approximation algorithms for monotone k-submodular function maximization with constraints","authors":"Yuying Li, Min Li, Yang Zhou, Shuxian Niu, Qian Liu","doi":"10.1007/s10878-025-01299-y","DOIUrl":"https://doi.org/10.1007/s10878-025-01299-y","url":null,"abstract":"<p>In recent years, <i>k</i>-submodular functions have garnered significant attention due to their natural extension of submodular functions and their practical applications, such as influence maximization and sensor placement. Influence maximization involves selecting a set of nodes in a network to maximize the spread of information, while sensor placement focuses on optimizing the locations of sensors to maximize coverage or detection efficiency. This paper first proposes two randomized algorithms aimed at improving the approximation ratio for maximizing monotone <i>k</i>-submodular functions under matroid constraints and individual size constraints. Under the matroid constraints, we design a randomized algorithm with an approximation ratio of <span>(frac{nk}{2nk-1})</span> and a complexity of <span>(O(rn(text {RO}+ktext {EO})))</span>, where <i>n</i> represents the total number of elements in the ground set, <i>k</i> represents the number of disjoint sets in a <i>k</i>-submodular function, <i>r</i> denotes the size of the largest independent set, <span>(text {RO})</span> indicates the time required for the matroid’s independence oracle, and <span>(text {EO})</span> denotes the time required for the evaluation oracle of the <i>k</i>-submodular function.Meanwhile, under the individual size constraints, we achieve an approximation factor of <span>(frac{nk}{3nk-2})</span> with a complexity of <i>O</i>(<i>knB</i>), where <i>n</i> is the total count of elements in the ground set, and <i>B</i> is the upper bound on the total size of the <i>k</i> disjoint subsets, belonging to <span>(mathbb {Z_{+}})</span>. Additionally, this paper designs two double randomized algorithms to accelerate the algorithm’s running speed while maintaining the same approximation ratio, with success probabilities of (<span>(1-delta )</span>), where <span>(delta )</span> is a positive parameter input by the algorithms. Under the matroid constraint, the complexity is reduced to <span>(O(nlog rlog frac{r}{delta }(text {RO}+ktext {EO})))</span>. Under the individual size constraint, the complexity becomes <span>(O(k^{2}nlog frac{B}{k}log frac{B}{delta }))</span>.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"123 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143933588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on the versions of artificial bee colony algorithm for scheduling problems","authors":"Beyza Gorkemli, Ebubekir Kaya, Dervis Karaboga, Bahriye Akay","doi":"10.1007/s10878-025-01296-1","DOIUrl":"https://doi.org/10.1007/s10878-025-01296-1","url":null,"abstract":"<p>Today, artificial bee colony (ABC) algorithm is one of the most popular swarm intelligence based optimization techniques. Although it was originally introduced to work on continuous space for numerical optimization problems, several researchers also successfully use the ABC for other problem types. In this study, variants of the ABC for scheduling problems are surveyed. Since the scheduling problems are combinatorial type problems, generally some modifications related to the solution representation or neighborhood search operators are introduced in these studies. Additionally, several enhancement ideas are also presented for the ABC algorithm such as the improvements of initialization, employed bee, onlooker bee, scout bee phases and hybrid usage with other metaheuristics or local search methods. This paper evaluates the literature, provides some analyses on its current state and gaps, and addresses possible future works. It is hoped that this review study would be beneficial for the researchers interested in this field.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"96 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Faster parameterized algorithms for variants of 3-Hitting Set","authors":"Dekel Tsur","doi":"10.1007/s10878-025-01300-8","DOIUrl":"https://doi.org/10.1007/s10878-025-01300-8","url":null,"abstract":"<p>In the <i>A</i><span>-Multi</span>3<span>-Hitting Set</span> problem (<i>A</i>-M3HS), where <span>(A subseteq {1,2,3})</span>, the input is a hypergraph <i>G</i> in which the hyperedges have sizes at most 3 and an integer <i>k</i>, and the goal is to decide if there is a set <i>S</i> of at most <i>k</i> vertices such that <span>(|S cap e| in A)</span> for every hyperedge <i>e</i>. In this paper we give <span>(O^*(2.027^k))</span>-time algorithms for <span>({1})</span>-M3HS and <span>({1,3})</span>-M3HS, and an <span>(O^*(1.381^k))</span>-time algorithm for <span>({2})</span>-M3HS.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"49 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scheduling problems with rejection in green manufacturing industry","authors":"Fanyu Kong, Jiaxin Song, Cuixia Miao, Yuzhong Zhang","doi":"10.1007/s10878-025-01295-2","DOIUrl":"https://doi.org/10.1007/s10878-025-01295-2","url":null,"abstract":"<p>Green manufacturing is used to describe an environmentally friendly manufacturing approach, which explicitly considers the impact of production on the environment and resources. Therefore, the production scheduling of solving energy conscious is in line with the focus of green manufacturing. In this paper, we consider the scheduling problems with rejection in the green manufacturing industry. The objective is to minimize the makespan of the accepted jobs plus the total rejection penalty of the rejected jobs, subject to the constraint that the total machine cost of the processed jobs is not more than a given threshold. We present pseudo-polynomial time algorithms and 2-approximation algorithms for the single-machine and the parallel-machine problems, respectively.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"3 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 3-space dynamic programming heuristic for the cubic knapsack problem","authors":"Ibrahim Dan Dije, Franklin Djeumou Fomeni, Leandro C. Coelho","doi":"10.1007/s10878-025-01294-3","DOIUrl":"https://doi.org/10.1007/s10878-025-01294-3","url":null,"abstract":"<p>The cubic knapsack problem (CKP) is a combinatorial optimization problem, which can be seen both as a generalization of the quadratic knapsack problem (QKP) and of the linear Knapsack problem (KP). This problem consists of maximizing a cubic function of binary decision variables subject to one linear knapsack constraint. It has many applications in biology, project selection, capital budgeting problem, and in logistics. The QKP is known to be strongly NP-hard, which implies that the CKP is also NP-hard in the strong sense. Unlike its linear and quadratic counterparts, the CKP has not received much of attention in the literature. Thus the few exact solution methods known for this problem can only handle problems with up to 60 decision variables. In this paper, we propose a deterministic dynamic programming-based heuristic algorithm for finding a good quality solution for the CKP. The novelty of this algorithm is that it operates in three different space variables and can produce up to three different solutions with different levels of computational effort. The algorithm has been tested on a set of 1570 test instances, which include both standard and challenging instances. The computational results show that our algorithm can find optimal solutions for nearly 98% of the standard test instances that could be solved to optimality and for 92% for the challenging instances. Finally, the computational experiments present comparisons between our algorithm, an existing heuristic algorithm for the CKP found in the literature, as well as adaptations to the CKP of some heuristic algorithms designed for the QKP. The results show that our algorithm outperforms all these methods.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"84 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing the 3-path vertex cover problem in selected graph classes","authors":"Sangram K. Jena, K. Subramani","doi":"10.1007/s10878-025-01285-4","DOIUrl":"https://doi.org/10.1007/s10878-025-01285-4","url":null,"abstract":"<p>In this paper, we focus on analyzing the 3-path vertex cover (3PVC) problem in a number of graph classes. Let <span>(G=(V,E))</span> be a simple graph. A set <span>(C subseteq V)</span> is called a <i>k</i>-path vertex cover of <i>G</i>, if each path of order <i>k</i> in <i>G</i>, contains at least one vertex from <i>C</i>. In the <i>k</i>-path vertex cover problem, we are given a graph <i>G</i>, and asked to find a <i>k</i>-path vertex cover of minimum size. For <span>(k=3)</span>, the problem becomes the well-known 3PVC problem. A problem that is closely related to the 3PVC problem is the dissociation set (DS) problem. Given a graph <span>(G=(V,E))</span>, a <i>dissociation set</i> is any <span>(D subseteq V)</span>, such that the vertex-induced subgraph <span>(G'= (D,E'))</span> consists of vertices having degree 0 or 1. In the dissociation set problem, we are required to find a dissociation set of maximum cardinality. Both these problems have also been studied extensively as per the literature. In this paper, we focus on pipartite (planar and bipartite) graphs for the most part. We first show that the 3PVC problem is <b>NP-hard</b>, even in pipartite graphs having maximum degree 4. We then show that the 3PVC problem on this class of graphs admits a linear time <span>(frac{8}{5})</span>-approximation algorithm. Next, we show that the 3PVC problem is <b>APX-complete</b> in bipartite graphs having maximum degree 4 and cubic graphs. Finally, we discuss an elegant and alternative proof for the <b>APX-completeness</b> of the vertex cover problem in cubic graphs and establish lower bounds for the 3PVC problem in special graph classes. It is important to note that our work is the first of its kind to establish <b>APX-completeness</b> of the 3PVC problem in graphs. </p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"28 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143880537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}