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Journal articles on the topic 'Hungarian algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Gabrovšek, Boštjan, Tina Novak, Janez Povh, Darja Rupnik Poklukar, and Janez Žerovnik. "Multiple Hungarian Method for k-Assignment Problem." Mathematics 8, no. 11 (2020): 2050. http://dx.doi.org/10.3390/math8112050.

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The k-assignment problem (or, the k-matching problem) on k-partite graphs is an NP-hard problem for k≥3. In this paper we introduce five new heuristics. Two algorithms, Bm and Cm, arise as natural improvements of Algorithm Am from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms, Dm, Em, and Fm, incorporate randomization. Algorithm Dm can be considered as a greedy version of Bm, whereas Em and Fm are versions of local search algorithm, specialized for the k-matching problem. The algorithms are implemented in Python and are run on three datasets. On the datasets available, all the algorithms clearly outperform Algorithm Am in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm Am) to 0.08% over optimum (algorithm Em). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm Am) to 0.45% over optimum (algorithm Fm). Better quality of solutions demands higher computation times, thus the new algorithms provide a good compromise between quality of solutions and computation time.

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Jonker, Roy, and Ton Volgenant. "Improving the Hungarian assignment algorithm." Operations Research Letters 5, no. 4 (1986): 171–75. http://dx.doi.org/10.1016/0167-6377(86)90073-8.

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3

Wright, M. B. "Speeding up the hungarian algorithm." Computers & Operations Research 17, no. 1 (1990): 95–96. http://dx.doi.org/10.1016/0305-0548(90)90031-2.

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4

Elias, Munapo. "DEVELOPMENT OF AN ACCELERATING HUNGARIAN METHOD FOR ASSIGNMENT PROBLEMS." Eastern-European Journal of Enterprise Technologies 4, no. 4 (106) (2020): 6–13. https://doi.org/10.15587/1729-4061.2020.209172.

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The Hungarian method is a well-known method for solving the assignment problem. This method was developed and published in 1955. It was named the Hungarian method because two theorems from two Hungarian mathematicians were used. In 1957, it was noticed that this algorithm is strongly polynomial and has a complexity of order O(n<sup>4</sup>) This is the reason why the Hungarian method is also known as the Kuhn-Munkres algorithm. Later on, in 1971 the complexity of the method was improved to order O(n<sup>3</sup>) A smallest uncovered element is selected to create a single zero at every iteration. This is a weakness and is alleviated by selecting more than one smallest uncovered element thus creating more than one zero at every iteration to come up with what we now call the Accelerating Hungarian (AH) method. From the numerical illustration of the Hungarian method given in this paper, we require 6 iterations to reach optimality. It can also be shown that selecting a single smallest uncovered element (e<sub>s</sub>) makes the Hungarian method inefficient when creating zeros. From the same numerical illustration of the proposed algorithm (AH) also given in this paper, it can be noted that only one iteration is required to reach optimality and that a total of six zeros are created in one iteration. Assignment model and the Hungarian method have application in addressing the Weapon Target Assignment (WTA) problem. This is the problem of assigning weapons to targets while considering the maximum probability of kill. The assignment problem is also used in the scheduling problem of physicians and medical staff in the outpatient department of large hospitals with multi-branches. The mathematical modelling of this assignment problem results in complex problems. A hybrid meta-heuristic algorithm SCA–VNS combining a Sine Cosine Algorithm (SCA) and Variable Neighbourhood Search (VNS) based on the Iterated Hungarian algorithm is normally used

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Cheng, Yuan Zeng, Pei Chao Zhang, and Bin Qian Cao. "Weapon Target Assignment Problem Solving Based on Hungarian Algorithm." Applied Mechanics and Materials 713-715 (January 2015): 2041–44. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2041.

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Weapon target assignment problem is most critical in modern warfare command decision of a problem for the weapon system and a relatively small number of targets assignment problem, you can use the Hungarian algorithm. Hungarian algorithm can solve the assignment problem, but under normal circumstances, weapon target assignment problem does not have the form of a mathematical model of assignment problem, through dummy weapon system or target method, the weapon target assignment problem is transformed into a standard assignment problem, and then solved by the Hungarian algorithm.

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El-ghorab, Mahmoud Ahmed, Mohamed Rihan El-meligy, Mohamed Mostafa Ibrahim, and Fatma Newagy. "Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks." Applied Sciences 12, no. 11 (2022): 5421. http://dx.doi.org/10.3390/app12115421.

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The motivations for deploying energy and spectral-efficient network architectures are the high energy consumption and the need for more spectral resources in modern cellular networks. The key method to solve the energy efficiency EE maximization problem of the downlink non-orthogonal multiple access (NOMA)-based massive MIMO system is to decouple it into user pairing and efficient power allocation problems. This work studies the performance of three main pairing methods in NOMA-based networks: Hungarian, Gale–Shapley, and correlation-based approaches. Firstly, we provide a mathematical analysis for EE of downlink NOMA in a massive MIMO system for the non-line of sight (NLoS) channel model with perfect successive interference cancellation (SIC). Finally, the sequential convex programming (SCP) approach is used to tackle the power allocation problem. Simulation results show that the Hungarian algorithm for pairing plus SCP for power allocation (Hungarian algorithm-SCP) achieves the highest energy efficiency among all the three pairing algorithms with an identical performance to joint user and resource block association with power allocation (joint user-RB PA) algorithm but with much lower computational complexity and outperforms the NOMA SCP greedy algorithm (NOMA-SCP-GA).

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Amalia, Ivanda Zevi, Ahmad Saikhu, and Rully Soelaiman. "A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems." Jurnal Online Informatika 6, no. 1 (2021): 118. http://dx.doi.org/10.15575/join.v6i1.692.

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The assignment problem is one of the fundamental problems in the field of combinatorial optimization. The Hungarian algorithm can be developed to solve various assignment problems according to each criterion. The assignment problem that is solved in this paper is a dynamic assignment to find the maximum weight on the resource allocation problems. The dynamic characteristic lies in the weight change that can occur after the optimal solution is obtained. The Hungarian algorithm can be used directly, but the initialization process must be done from the beginning every time a change occurs. The solution becomes ineffective because it takes up a lot of time and memory. This paper proposed a fast dynamic assignment algorithm based on the Hungarian algorithm. The proposed algorithm is able to obtain an optimal solution without performing the initialization process from the beginning. Based on the test results, the proposed algorithm has an average time of 0.146 s and an average memory of 4.62 M. While the Hungarian algorithm has an average time of 2.806 s and an average memory of 4.65 M. The fast dynamic assignment algorithm is influenced linearly by the number of change operations and quadratically by the number of vertices.

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VIZUETE-LUCIANO, Emili, José M. MERIGÓ, Anna M. GIL-LAFUENTE, and Sefa BORIA-REVERTER. "DECISION MAKING IN THE ASSIGNMENT PROCESS BY USING THE HUNGARIAN ALGORITHM WITH OWA OPERATORS." Technological and Economic Development of Economy 21, no. 5 (2015): 684–704. http://dx.doi.org/10.3846/20294913.2015.1056275.

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Assignment processes permit to coordinate two set of variables so each variable of the first set is connected to another variable of the second set. This paper develops a new assignment algorithm by using a wide range of aggregation operators in the Hungarian algorithm. A new process based on the use of the ordered weighted averaging distance (OWAD) operator and the induced OWAD (IOWAD) operator in the Hungarian algorithm is introduced. We refer to it as the Hungarian algorithm with the OWAD operator (HAOWAD) and the Hungarian algorithm with the IOWAD operator (HAIOWAD). The main advantage of this approach is that we can provide a parameterized family of aggregation operators between the minimum and the maximum. Thus, the information can be represented in a more complete way. Furthermore, we also present a general framework by using generalized and quasi-arithmetic means. Therefore, we can consider a wide range of particular cases including the Euclidean and the Minkowski distance. The paper ends with a practical application of the new approach in a financial decision making problem regarding the assignment of investments.

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Aparovich, U. A. "Association of the Tracked Trajectories and Marks by the Attraction Method During Secondary Processing of Radar Information." Doklady BGUIR 20, no. 7 (2022): 65–71. http://dx.doi.org/10.35596/1729-7648-2022-20-7-65-71.

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The article proposes a new algorithm for associating the tracked trajectories and newly received marks by coordinates during the secondary processing (track-while-scan) of radar information. It is known that the biggest difficulties arise when associating in dense groups, that is, when the distance between the trajectories is commensurate with the errors in measuring their coordinates. Usually, well-known methods for solving the assignment task are used, for example, the Hungarian algorithm and similar. A common disadvantage of these methods is a rapid increase (in proportion to the third or fourth degree of the number of trajectories) in the time for solving the task. The article proposes to use the “attraction” method to increase the speed of calculations. The proposed algorithm simulates the “attraction” of all trajectories to all marks and the mutual “repulsion” of all trajectories from each other (the position of the trajectories is extrapolated to the time of location of the marks.) The conditional “movement” of the trajectories is simulated step by step until a set approach to any marks happens. Comparative modeling of the attraction algorithm and the Hungarian algorithm in the case of equal number of trajectories and marks showed that the qualitative characteristics of the algorithms are approximately the same, but the execution time for the attraction algorithm grows more slowly than for the Hungarian algorithm (in proportion to the square of the number of trajectories). Therefore, with a large number of them (more than 100–300), the attraction algorithm is executed much faster. Obviously, with the corresponding adjustment of the value and dimensions of the parameters, the new algorithm can be used to solve other assignment tasks.

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10

Xue, Kai, Zhiqin Huang, Ping Wang, and Zeyu Xu. "An Exact Algorithm for Task Allocation of Multiple Unmanned Surface Vehicles with Minimum Task Time." Journal of Marine Science and Engineering 9, no. 8 (2021): 907. http://dx.doi.org/10.3390/jmse9080907.

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Task allocation of unmanned surface vehicles (USVs) with low task cost is an important research area which assigns USVs from starting points to different target points to complete tasks. Most of the research lines of task allocation are using heuristic algorithms to obtain suboptimal solutions to reduce both the max task cost and total task cost. In practice, reducing the maximum is more important to task time, which is from the departure of USVs to the last USV arriving at the designated position. In this paper, an exact algorithm is proposed to minimize the max task time and reduce the total task time based on the Hungarian algorithm. In this algorithm, task time is composed of the travel time along the planned path and the turning time at initial and target points. The fast marching square method (FMS) is used to plan the travel path with obstacle avoidance. The effectiveness and practicability of the proposed algorithm are verified by comparing it with the Hungarian algorithm (HA), the auction algorithm (AA), the genetic algorithm (GA) and the ant colony optimization algorithm (ACO). The results of path planning and task allocation are displayed in the simulation.

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Juan, Miguel J. Bawagan. "A Kidney Exchange Matching Application Using the Blossom and Hungarian Algorithms for Pairwise and Multiway Matching." Indian Journal of Science and Technology 13, no. 2 (2020): 229–47. https://doi.org/10.17485/ijst/2020/v13i02/149446.

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Abstract <strong>Background/objectives:</strong> Patients of kidney failure sometimes have incompatible donors. This study proposes an application to get the best matchings based on scoring data. <strong>Methods:</strong> For pairwise matching, we created a new graph from the original scoring matrix. This graph ensures pairwise matchings. To find an optimal matching, we used the Blossom algorithm. For multiway matching, we interpreted the scoring matrix as an assignment problem. For this, we used the Hungarian algorithm. The application was created using Python, NetworkX, NumPy, and PySimpleGUI. The app uses CSV files as input. <strong>Findings:</strong> Both algorithms made for polynomial run times. Matching is fast and is guaranteed to be optimal. The application itself gets instantaneous results even for large donor-patient matrices. <strong>Application/improvement:</strong> Hospitals or organizations can use this application for their kidney matching programs.  <strong>Keywords:</strong> Blossom Algorithm, Hungarian Algorithm, Kidney Failure, Donor Matching.  

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Woźniak, Waldemar, Maciej Niedziela, Roman Stryjski, and Janusz Mielniczuk. "Selected methods for the improvement of economic efficiency for obtaining orders from electronic transport exchanges." WUT Journal of Transportation Engineering 127 (December 1, 2019): 93–103. http://dx.doi.org/10.5604/01.3001.0013.7294.

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The article presents the research problem that was considered in the context of the analysis and selection of the optimization algorithms used to solve the task of assigning vehicles to transport orders and their economic efficiency. Two optimization algorithms were compared and used for the present analysis. The first is the Hungarian Algorithm, a classic transport algorithm used in most TMS systems, Transportation Management Systems. The authors developed the second based on heuristic methods and implemented in selected transport companies under the name: improvement algorithm.

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Chi-Jen, Lin, and Lin Wan-Ting. "A systematic weighted-Hungarian-algorithm for optimization and postoptimal analysis of transportation problem." Journal of Statistics and Management Systems 26, no. 4 (2023): 843–66. http://dx.doi.org/10.47974/jsms-936.

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This paper first proposes an easy algorithm for optimizing the transportation problem. Then, optimization procedures of the algorithm are applied for sensitivity analysis and the parametric analysis. The efficient algorithm can be proceeded systematically and smoothly. Some numerical examples are given to demonstrate these procedures. The attractive features of the new algorithms include: (1) The algorithm for solving the optimal solution of the transportation problem is an easy weighted Hungarian algorithm that can be applied to transportation problem and assignment problem; (2) The algorithm conquer the obstacles, e.g. cycling and stalling etc., caused by degeneracy; (3) The algorithm is capable of solving the large scale transportation problem; (4) The algorithm is easily expended to identify the Type I, Type II and Type III sensitivity range perturbing one cost coefficient; (5) The algorithm can reoptimize by using the original optimal table when variation exceeding sensitivity range, needless to resolve from scratch; (6)The algorithm can perform the parametric analysis and determine the optimal value function perturbing one or multiple cost coefficients.

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Blatov, Igor A., Elena V. Kitaeva, Alexander P. Shevchenko, and Vladislav A. Blatov. "A universal algorithm for finding the shortest distance between systems of points." Acta Crystallographica Section A Foundations and Advances 75, no. 6 (2019): 827–32. http://dx.doi.org/10.1107/s2053273319011628.

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Three universal algorithms for geometrical comparison of abstract sets of n points in the Euclidean space R 3 are proposed. It is proved that at an accuracy ∊ the efficiency of all the algorithms does not exceed O(n 3/∊3/2). The most effective algorithm combines the known Hungarian and Kabsch algorithms, but is free of their deficiencies and fast enough to match hundreds of points. The algorithm is applied to compare both finite (ligands) and periodic (nets) chemical objects.

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Dehnavi, Sima, Madjid Emamipour, and Amin Golabpour. "Combining Random Forest and Neural Networks Algorithms to Diagnose Heart Disease." Frontiers in Health Informatics 9, no. 1 (2020): 34. http://dx.doi.org/10.30699/fhi.v9i1.214.

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Introduction: Heart disease is known as one of the most important causes of death in today's society and so far no definitive method has been found to predict it and several factors are effective in contracting this disease. Therefore, the aim of this study was to provide a data mining model for predicting heart disease.Material and Methods: This study used standard data from UCI. These data include four Cleveland, Hungarian, Swiss and Long Beach VA databases. These data include 13 independent variables and one dependent variable. The data are missing, and the EM algorithm was used to control this loss, and at the end of the data, a suggestion algorithm was implemented that combined the two random forest algorithms and the artificial neural network.Results: In this study, data was divided into two training sets and 10-Fold method was used. To evaluate the algorithms, three indicators of sensitivity, specificity, accuracy were used and the accuracy of the prediction algorithm for four data Cleveland, Hungarian, Switzerland and Long Beach VA reached 87.65%, 94.37%, 93.45% and 85%, respectively. Then, the proposed algorithm was compared with similar articles in this field, and it was found that this algorithm is more accurate than similar methods.Conclusion: The results of this study showed that by combining the two algorithms of random forest and artificial neural network, a suitable model for predicting heart attacks can be provided.

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Serratosa, Francesc. "Speeding up Fast Bipartite Graph Matching Through a New Cost Matrix." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 02 (2015): 1550010. http://dx.doi.org/10.1142/s021800141550010x.

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Bipartite (BP) has been seen to be a fast and accurate suboptimal algorithm to solve the Error-Tolerant Graph Matching problem. Recently, Fast Bipartite (FBP) has been presented that obtains the same distance value and node labelings but in a reduced time. Both algorithms approximate the quadratic problem in a linear problem and they do it through a specific cost matrix. FBP imposes the Edit costs to be defined such as the Edit distance is a distance function. Originally, the Hungarian method was used but it has been seen the Jonker–Volgenant linear solver obtains similar results than the Hungarian method but with an important run time reduction. Nevertheless, this second solver has some convergence problems on some specific cost matrices. The aim of this paper is to define a new cost matrix such that the Jonker–Volgenant solver converges and the matching algorithm obtains the same distance value than the BP algorithm.

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Jung, Chanju, Yong-Hyuk Kim, Yourim Yoon, and Byung-Ro Moon. "A New Adaptive Hungarian Mating Scheme in Genetic Algorithms." Discrete Dynamics in Nature and Society 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/3512546.

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In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.

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Irfan Bala, Mohammad, and Mohammad Ahsan Chishti. "Load Balancing in Cloud Computing Using Hungarian Algorithm." International Journal of Wireless and Microwave Technologies 9, no. 6 (2019): 1–10. http://dx.doi.org/10.5815/ijwmt.2019.06.01.

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Gdeisat, Munther, Miguel Arevalillo-Herráez, David Burton, and Francis Lilley. "Three-dimensional phase unwrapping using the Hungarian algorithm." Optics Letters 34, no. 19 (2009): 2994. http://dx.doi.org/10.1364/ol.34.002994.

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Kuncheva, Ludmila I. "Full-class set classification using the Hungarian algorithm." International Journal of Machine Learning and Cybernetics 1, no. 1-4 (2010): 53–61. http://dx.doi.org/10.1007/s13042-010-0002-z.

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PROF., S. G. SANMUKH, and SOMAYYA S. AVADUT MR. "RUBIKS CUBE ALGORITHM." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 7 (2018): 11–17. https://doi.org/10.5281/zenodo.1445428.

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<strong>The Rubiks Cube is a 3 - D mechanical puzzle originally called the Magic Cube. Invited in 1974 by Erno Rubik . He is a Hungarian inventor,architect and professor of architecture . Rubik cube is mind game . This game is very interesting and smartness game,this game is won the G er man game of the year. This games aim is all the same colors are in one said then the game is complete</strong> <strong>https://www.ijiert.org/paper-details?paper_id=141309</strong>

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Ombongi, F. O., H. O. Absaloms, and P. L. Kibet. "Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 6152–60. http://dx.doi.org/10.48084/etasr.3727.

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The current trend has seen the data capacity and traffic density increase due to the increased demand for multimedia services. Since this cannot be handled successfully by the current 4G networks, there is a need to integrate the mmWave and the Device-to-Device (D2D) communication 5G technologies to meet this increased demand and traffic density. However, there is the challenge of increased interference between dense D2D users and cellular users if D2D users are allowed to reuse the resources allocated to cellular users. This degrades the performance of the D2D users in terms of achievable data rate and Energy Efficiency (EE). The paper formulates a match theoretic resource allocation scheme to maximize the achievable D2D sum rate. In addition, an EE optimization problem is formulated for D2D users by considering the rate and power constraints. The EE optimization problem is solved by the Lagrangian dual decomposition method. The algorithms were simulated in MATLAB and the results were compared to Hungarian and heuristic optimization algorithms. The results showed that the match theoretic resource allocation is on average 1.82 times better than the Hungarian algorithm. At the same time, the match theoretic resource allocation algorithm increases fairness in resource allocation as it maintains a higher sum rate for low and high-density number of users. The proposed EE optimization algorithm improved the D2D performance by 8.2% compared to the heuristic algorithm.

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Ombongi, F. O., H. O. Absaloms, and P. L. Kibet. "Energy Efficient Resource Allocation in Millimeter-Wave D2D Enabled 5G Cellular Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 6152–60. https://doi.org/10.5281/zenodo.4016302.

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The current trend has seen the data capacity and traffic density increase due to the increased demand for multimedia services. Since this cannot be handled successfully by the current 4G networks, there is a need to integrate the mmWave and the Device-to-Device (D2D) communication 5G technologies to meet this increased demand and traffic density. However, there is the challenge of increased interference between dense D2D users and cellular users if D2D users are allowed to reuse the resources allocated to cellular users. This degrades the performance of the D2D users in terms of achievable data rate and Energy Efficiency (EE). The paper formulates a match theoretic resource allocation scheme to maximize the achievable D2D sum rate. In addition, an EE optimization problem is formulated for D2D users by considering the rate and power constraints. The EE optimization problem is solved by the Lagrangian dual decomposition method. The algorithms were simulated in MATLAB and the results were compared to Hungarian and heuristic optimization algorithms. The results showed that the match theoretic resource allocation is on average 1.82 times better than the Hungarian algorithm. At the same time, the match theoretic resource allocation algorithm increases fairness in resource allocation as it maintains a higher sum rate for low and high-density number of users. The proposed EE optimization algorithm improved the D2D performance by 8.2% compared to the heuristic algorithm.

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Fareha, Nizam, Roslee Mardeni, Yusoff Zubaida, et al. "Efficient radio resource allocation scheme for 5G networks with device-to-device communication." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5588–600. https://doi.org/10.11591/ijece.v11i6.pp5588-5600.

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A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.

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Chi, Zhang, Zhang Chun, Wang Yan, and Hu Hao. "Research on fire distribution algorithm based on Hopfield neural network." Journal of Physics: Conference Series 2478, no. 9 (2023): 092006. http://dx.doi.org/10.1088/1742-6596/2478/9/092006.

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Abstract the problem of air defense fire distribution in ECM command and control system is studied. Based on the mathematical model of fire distribution, the classical solution algorithm Hungarian method is introduced, and the shortcomings of Hungarian method in solving fire distribution are pointed out. According to the basic principle of Hopfield neural network model, the algorithm process of solving specific optimization problems is deduced, and the fire distribution problem is simulated. According to the simulation results, the application conclusion and improved method of Hopfield neural network model in fire distribution problem are given, which proves that the model can solve the fire distribution problem more effectively.

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Kusza, S., I. Nagy, T. Németh, A. Molnár, A. Jávor, and S. Kukovics. "The genetic variability of Hungarian Tsigai sheep." Archives Animal Breeding 53, no. 3 (2010): 309–17. http://dx.doi.org/10.5194/aab-53-309-2010.

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Abstract. Microsatellite analysis was used to estimate the genetic origin, differences, relationship within 10 Hungarian Tsigai populations. The number of alleles was 262 at the 16 examined locus. Fifteen population specific alleles were detected. The mean number of alleles detected per locus ranged from 4.3 (OarAE119) to 11.9 (MAF70). Genetic distance values were calculated from Nei’s minimum genetic distance (DA) formula. Phylogenetic tree was constructed using UPGMA algorithm. The results indicated that the genetic difference was negligible between the following populations pair-wise: two Hungarian indigenous populations (Kardoskút1-IN and Kardoskút2-IN); the Hungarian indigenous population Soltszentimre-IN and the Milking Tsigai population Akasztó-ZO; the Hungarian indigenous population Csanádpalota-IN and the transitional type population Makó-Rákos-TR. Microsatellite genotyping prooved to be efficient tool for examing the genetic relationships among Hungarian Tsigai populations.

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Shen, XiaoDong, and Lei Chen. "Configuration Transformation Planning Method of Unmanned Aerial Vehicle Cluster Based on Improved Hungarian Algorithm." Journal of Physics: Conference Series 2283, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2283/1/012001.

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Abstract For the planning problem of Unmanned Aerial Vehicle (UAV) formation configuration transformation, a formation configuration transformation planning method based on the improved Hungarian algorithm is proposed. By selecting a straight-line path as the movement path of UAVs in configuration transformation, the configuration transformation problem is transformed into a multi-UAV position assignment problem with constraint handling in a dynamic environment. For the Hungarian algorithm efficiency matrix power function treatment, the improved algorithm can be simultaneously bivariate integrated optimization; Introduce the time axis in the configuration space to establish the configuration-time space, and analyze the collision constraints during the dynamic transformation process in this space, and finally, the feasibility of the method is verified by simulation.

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Shen, XiaoDong, and Lei Chen. "Configuration Transformation Planning Method of Unmanned Aerial Vehicle Cluster Based on Improved Hungarian Algorithm." Journal of Physics: Conference Series 2283, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2283/1/012001.

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Abstract For the planning problem of Unmanned Aerial Vehicle (UAV) formation configuration transformation, a formation configuration transformation planning method based on the improved Hungarian algorithm is proposed. By selecting a straight-line path as the movement path of UAVs in configuration transformation, the configuration transformation problem is transformed into a multi-UAV position assignment problem with constraint handling in a dynamic environment. For the Hungarian algorithm efficiency matrix power function treatment, the improved algorithm can be simultaneously bivariate integrated optimization; Introduce the time axis in the configuration space to establish the configuration-time space, and analyze the collision constraints during the dynamic transformation process in this space, and finally, the feasibility of the method is verified by simulation.

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Moon, SungTae. "Fair Hungarian Algorithm for Swarming Drone Flight Formation Transformation." Journal of KIISE 49, no. 6 (2022): 459–65. http://dx.doi.org/10.5626/jok.2022.49.6.459.

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Gong, Xunan, Hongmei Li, and Xuesheng Yang. "A Task Planning Simulation Method Based On Hungarian Algorithm." Journal of Physics: Conference Series 2219, no. 1 (2022): 012041. http://dx.doi.org/10.1088/1742-6596/2219/1/012041.

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Abstract As information technology continues to evolve, the need for accuracy and efficiency in task planning in computer simulation continues to strengthen. Task planning is a key part of computer-generated resource planning. To improve the effectiveness of task planning, this paper tries to design a task planning method. Firstly, a task planning model is designed by decomposing the requirements of the task according to the task capability of the platform, describing the resources quantitatively, and extracting the constraints and objective functions. Then the Hungarian algorithm is used to solve the problem. Finally, a typical case is used to test the proposed method, which verifies the correctness and feasibility of the method.

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Berberler, Murat Ersen, Onur Ugurlu, and Gozde Kizilates. "On a Subroutine for Covering Zeros in Hungarian Algorithm." Pamukkale University Journal of Engineering Sciences 18, no. 2 (2012): 85–94. http://dx.doi.org/10.5505/pajes.2012.30502.

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Kume, Alfred, and Stephen G. Walker. "The utility of clusters and a Hungarian clustering algorithm." PLOS ONE 16, no. 8 (2021): e0255174. http://dx.doi.org/10.1371/journal.pone.0255174.

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Implicit in the k–means algorithm is a way to assign a value, or utility, to a cluster of points. It works by taking the centroid of the points and the value of the cluster is the sum of distances from the centroid to each point in the cluster. The aim in this paper is to introduce an alternative way to assign a value to a cluster. Motivation is provided. Moreover, whereas the k–means algorithm does not have a natural way to determine k if it is unknown, we can use our method of evaluating a cluster to find good clusters in a sequential manner. The idea uses optimizations over permutations and clusters are set by the cyclic groups; generated by the Hungarian algorithm.

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Wang, Zaili, Zhiyong Feng, and Ping Zhang. "An Iterative Hungarian Algorithm Based Coordinated Spectrum Sensing Strategy." IEEE Communications Letters 15, no. 1 (2011): 49–51. http://dx.doi.org/10.1109/lcomm.2010.111910.101806.

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Cao, Bin, Jiaxing Wang, Jing Fan, Jianwei Yin, and Tianyang Dong. "Querying Similar Process Models Based on the Hungarian Algorithm." IEEE Transactions on Services Computing 10, no. 1 (2017): 121–35. http://dx.doi.org/10.1109/tsc.2016.2597143.

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Goldberger, Jacob, and Tamir Tassa. "A hierarchical clustering algorithm based on the Hungarian method." Pattern Recognition Letters 29, no. 11 (2008): 1632–38. http://dx.doi.org/10.1016/j.patrec.2008.04.003.

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Lopes, Paulo A. C., Satyendra Singh Yadav, Aleksandar Ilic, and Sarat Kumar Patra. "Fast block distributed CUDA implementation of the Hungarian algorithm." Journal of Parallel and Distributed Computing 130 (August 2019): 50–62. http://dx.doi.org/10.1016/j.jpdc.2019.03.014.

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Liu, Jiancheng, Jian Wu, and Chengcheng Zhang. "Research on AGV Scheduling Optimization Based on Hungarian Algorithm." Journal of Physics: Conference Series 1550 (May 2020): 032041. http://dx.doi.org/10.1088/1742-6596/1550/3/032041.

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Kozachko, O. M., Y. M. Kryzhanovskyi, S. O. Zhukov, and I. V. Varchuk. "IMPROVEMENT OF ASSIGNING TASKS METHOD FOR THE VEHICLE MAINTENANCE EMPLOYEES BASED ON GENETIC AND HUNGARIAN ALGORITHMS." Information technology and computer engineering 57, no. 2 (2023): 25–32. http://dx.doi.org/10.31649/1999-9941-2023-57-2-25-32.

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The method of automated process of assigning tasks to employees of vehicle service stations based on genetic and Hungarian algorithms has been improved, which, unlike existing ones, takes into account the complexity of the task, the time of task execution and the qualifications of workers, and also allows to speed up and optimize the workflow at vehicle service stations. To evaluate the optimality of solution options, a new criterion is proposed, which, in addition to the qualifications of the worker, the complexity and time of the task, allows taking into account the needs of the enterprise in different seasons. The experimental data of the proposed algorithms were computerized. The initial data for the computer experiment were taken as data on the functioning of a real service station in Vinnytsia with and without the automated application of an improved method of assigning tasks to employees of a vehicle service station based on genetic and Hungarian algorithms. Computer experiments have shown that genetic algorithm work better with a large number of tasks, and the Hungarian algorithm works better with a small number of tasks. On the basis of the proposed improvements and algorithms, a cross-platform automated system for vehicle service station employees has been developed, which, unlike existing ones, provides instant interaction between the system's software modules, thanks to the microservice architecture and takes into account the high load of client requests, due to the horizontal scaling of the servers that host the system software. A special feature of the automated system is that it provides station employees with an automated workplace where they can manage their own tasks and monitor and control their execution, which allows vehicle service station owners to control the entire customer service process and correctly prioritize tasks for their employees.

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Nizam, Fareha, Mardeni Roslee, Zubaida Yusoff, et al. "Efficient radio resource allocation scheme for 5G networks with device-to-device communication." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5588. http://dx.doi.org/10.11591/ijece.v11i6.pp5588-5600.

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<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

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Feng, Yuliang, Wei Ma, Yu Tan, et al. "Approach of Dynamic Tracking and Counting for Obscured Citrus in Smart Orchard Based on Machine Vision." Applied Sciences 14, no. 3 (2024): 1136. http://dx.doi.org/10.3390/app14031136.

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The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards.

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Murugesan, R. "E-SOFT - A VERY SIMPLE AND INNOVATIVE METHOD FOR SOLVING UNBALANCED ASSIGNMENT PROBLEMS." International Journal of Advanced Research 10, no. 05 (2022): 994–1005. http://dx.doi.org/10.21474/ijar01/14806.

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In this paper, we have proposed a noveland innovative method entitled E-SOFT fordetermining the optimal assignment plans to the unbalanced assignment problems (UAPs) and viewed its performance with the existing Hungarian method and the Mantra technique. The ESOFT algorithm has beeninheritedfrom the existing SOFTMIN algorithm developed for solving transportation problems and also extended for solving the UAPs.The Extended SOFTMIN method is in short termed as E-SOFT method. The performance of the proposed E-SOFT method over the Hungarian and Mantra methods has been tested on a set of 20 identified UAPs. Experimental results validate that the E-SOFT is an alternative simple method to solve UAPs.

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Gao, Yangyang, Minjian Yu, Hanghang You, and Huiming Ji. "A New Method of Command and Guide the Unbalanced Distribution of Target." MATEC Web of Conferences 214 (2018): 03001. http://dx.doi.org/10.1051/matecconf/201821403001.

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In the process of air combat implementation, the real-time command and guidance of air pilot is the key element of air combat irreplaceable. In this paper, a multi-aircraft air combat command and guidance target allocation model is established based on the actual air combat situation. The unbalanced target allocation problem of multi-aircraft air combat command and guidance is studied by combining Hungarian algorithm and evolutionary Hungarian algorithm. Finally, two cases of increasing aircraft number and decreasing aircraft number are simulated and studied. Simulation results show that the method can shorten the computation time and ensure the overall command and guidance advantages of the target allocation scheme effectively.

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Martin, Antonyraj, and Veeramani V. "hybrid approach of Hungarian method to find optimal solution for solving Fuzzy Transportation Problem using Hexagonal Fuzzy numbers." International Journal of Information Technology, Research and Applications 2, no. 2 (2023): 61–64. http://dx.doi.org/10.59461/ijitra.v2i2.59.

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This research article explains about a hybrid approach of Hungarian method to solve Hexagonal Fuzzy Transportation Problem. Step by step algorithm is given in this article. A numerical illustration is given to verify the new hybrid algorithm. Within short duration, optimal solution is derived easily.

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Abbass, Waseem, Riaz Hussain, Jaroslav Frnda, Nasim Abbas, Muhammad Awais Javed, and Shahzad A. Malik. "Resource Allocation in Spectrum Access System Using Multi-Objective Optimization Methods." Sensors 22, no. 4 (2022): 1318. http://dx.doi.org/10.3390/s22041318.

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The paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550–3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels’ attributes and the diversification of GAA users’ business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale–Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.

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Liu, Kai Ming, Chun Qing Ruan, Yuan An Liu, Xiao Yu Cheng, and Fang Liu. "An Efficient Joint Subcarrier Pairing and Resource Allocation for Multi-Relay OFDM Systems." Applied Mechanics and Materials 696 (November 2014): 207–14. http://dx.doi.org/10.4028/www.scientific.net/amm.696.207.

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For multi-relay orthogonal frequency division multiplexing (OFDM) systems, efficient subcarrier pairing and resource allocation are significant to boost the system performance. We study the joint optimization problem of subcarrier assignment, subcarrier pairing and power allocation, to maximize the system transmission rate under a total power constraint. Many previous works formulate such a problem as a mixed integer programming problem, then transform it into a convex optimization problem by continuous relaxation and pair all the subcarriers using Hungarian method, which requires high computation complexity. In this paper, we assign each subcarrier in both hops to the relay that has the highest channel gain on that subcarrier. Since the numbers of subcarriers assigned to each relay in both hops, denoted as n1(k) and n2(k), are probably different, we first sort the subcarriers in descending order and pair the front min {n1(k),n2(k)} subcarriers assigned to each relay in sorted way, then we assign and pair the remaining subcarriers using Hungarian method. Due to the decrease of matrix dimensions when using Hungarian method, the computation complexity of the suboptimal algorithm is reduced. Simulation results show that the suboptimal algorithm provides the similar performance to that of the optimal algorithm with much lower computation complexity.

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Li, Juan, Yanxin Zhang, and Wenbo Li. "Formation Control of a Multi-Autonomous Underwater Vehicle Event-Triggered Mechanism Based on the Hungarian Algorithm." Machines 9, no. 12 (2021): 346. http://dx.doi.org/10.3390/machines9120346.

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Among the key technologies of Autonomous Underwater Vehicle (AUV) leader–follower formations control, formation reconfiguration technology is one of the main technologies to ensure that multiple AUVs successfully complete their tasks in a complex operating environment. The biggest drawback of the leader–follower formations technology is the failure of the leader and the excessive communication pressure of the leader. Aiming at the problem of leader failure in multi- AUV leader–follower formations, the Hungarian algorithm is used to reconstruct the failed formation with a minimum cost, and the improvement of the Hungarian algorithm can solve the problem of a non-standard assignment. In order to solve the problem of an increased leader communication task after formation reconfiguration, the application of an event-triggered mechanism (ETM) can reduce unnecessary and useless communication, while the efficiency of the ETM can be improved through increasing the event-triggered conditions of the sampling error threshold. The simulation results of multi-AUV formation control show that the Hungarian algorithm proposed in this paper can deal with the leader failure in the multi-AUV leader–follower formation, and the ETM designed in this paper can reduce about 90% of the communication traffic of the formation which also proves the highly efficient performance of the improved ETM in the paper.

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Sun, Ming, Kangle Zhai, Wei Cao, Ying Wang, and Yaoqun Xu. "Hybrid OFDMA Resource Allocation Scheme for Ensuring Required Level of Proportional Fairness." Mathematical Problems in Engineering 2020 (October 22, 2020): 1–19. http://dx.doi.org/10.1155/2020/5201545.

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The new-generation wireless communication networks are envisioned to offer higher sum data rates along with the required level of fairness. Previous works tend to suffer from a decayed performance as subcarriers become relatively insufficient in allocation to users. To maximize the sum data rates and ensure the required level of proportional fairness, this paper presents a hybrid OFDMA resource allocation scheme which uses Hungarian algorithm combined with a greedy method for subcarrier allocation and uses bee colony optimization for power allocation. The proposed subcarrier allocation scheme can make full use of advantages of both globally optimal Hungarian algorithm in enhancing sum data rates and locally optimal greedy method in maintaining a reasonable fairness level and can make Hungarian algorithm work in a searching mode for further improvement of sum data rates and fairness. The proposed power allocation scheme can converge to the required level of proportional fairness but with higher sum data rates if the subcarrier allocation does not achieve the required fairness. Simulation results show that the proposed scheme can obtain the required level of proportional fairness but with higher sum data rates even if subcarriers are relatively insufficient in allocation to users. Complexity analysis shows the proposed method has moderate complexity.

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Elsisy, M. A., A. S. Elsaadany, and M. A. El Sayed. "Using Interval Operations in the Hungarian Method to Solve the Fuzzy Assignment Problem and Its Application in the Rehabilitation Problem of Valuable Buildings in Egypt." Complexity 2020 (September 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/9207650.

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Assignment problem (AP) is an entrenched tool for solving engineering and management problems. The Hungarian method is always used to fathom the AP in crisp cases. This paper presents an algorithm of finding the optimum solution of the fuzzy AP by using the modified Hungarian method. This method is utilized to get a minimum assignment cost in the fuzzy environment for a fuzzy AP. Firstly, we use the fuzzy numbers without any transformation. Secondly, the fuzzy AP is transformed into an interval AP based on the α-cut methodology. Then, the interval arithmetic operations and the order relations are applied to get the optimal solution by utilizing the modified Hungarian method. The proposed algorithm requires less efforts and time to reach optimality, compared to the existing methods. Numerical examples are given to ensure the efficiency and the validity of the proposed algorithm. A study on the reuse of many valuable buildings is presented after using different government and service buildings in a manner that does not fit with its original function and heritage value. We offer a solution based on the scientific and realistic basis for requalification according to the variables and requirements of the social, cultural, and economic environments in the environment surrounding the valuable buildings.

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Yang, Guan Ci, Shao Bo Li, Yong Zhong, and Wei Jie Pan. "Automatic Synthesis of Dynamic Systems Based on Hungarian Algorithm and Genetic Programming." Key Engineering Materials 467-469 (February 2011): 160–63. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.160.

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Genetic programming is an effective way to generate design candidates in an open-ended, but statistically structured, manner. A critical aspect of the procedure is a fitness measure, which guides candidate designs toward an optimal scheme in reasonable time. This paper has suggested a new definition of fitness base on Hungarian algorithm for automatically synthesizing designs for multi-domain, lumped parameter dynamic systems, and uses a type of embryo bond graph model with three modifiable sites to initialize population. Although the experiments run to date are not sufficient to allow making strong statistical assertions, it shows that the search capability of genetic programming combining Hungarian algorithm is good enough to make feasible automated design methodology proposed here for multi-domain systems.

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Yadaiah, Ventepaka, and V. V. Haragopal. "Multi-Objective Optimization of Time-Cost-Quality Using Hungarian Algorithm." American Journal of Operations Research 06, no. 01 (2016): 31–35. http://dx.doi.org/10.4236/ajor.2016.61005.

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