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Volume 14
Issue 4
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Algorithms, Volume 14, Issue 4 (April 2021) – 27 articles

Cover Story (view full-size image): A new parametric family of three-step iterative procedures for approximating the solutions of nonlinear equations is presented. The analysis of convergence proves the sixth-order of all members of the class. Using complex dynamics, wide areas of the complex plane are found where the corresponding value of the parameter yields a stable iterative scheme. Moreover, some other regions are found whose respective iterative methods have chaotic or unstable behavior. Several test functions are analyzed to check the analytical and dynamical information. Some of the proposed methods are shown to be very efficient and can converge from initial estimations far from the searched solution. View this paper.
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22 pages, 4176 KiB  
Article
A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
by George Odongo, Richard Musabe and Damien Hanyurwimfura
Algorithms 2021, 14(4), 128; https://doi.org/10.3390/a14040128 - 20 Apr 2021
Cited by 19 | Viewed by 4090
Abstract
This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a [...] Read more.
This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data. Full article
(This article belongs to the Special Issue Supervised and Unsupervised Classification Algorithms)
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19 pages, 546 KiB  
Article
Branching Densities of Cube-Free and Square-Free Words
by Elena A. Petrova and Arseny M. Shur
Algorithms 2021, 14(4), 126; https://doi.org/10.3390/a14040126 - 20 Apr 2021
Cited by 1 | Viewed by 2360
Abstract
Binary cube-free language and ternary square-free language are two “canonical” representatives of a wide class of languages defined by avoidance properties. Each of these two languages can be viewed as an infinite binary tree reflecting the prefix order of its elements. We study [...] Read more.
Binary cube-free language and ternary square-free language are two “canonical” representatives of a wide class of languages defined by avoidance properties. Each of these two languages can be viewed as an infinite binary tree reflecting the prefix order of its elements. We study how “homogenious” these trees are, analysing the following parameter: the density of branching nodes along infinite paths. We present combinatorial results and an efficient search algorithm, which together allowed us to get the following numerical results for the cube-free language: the minimal density of branching points is between 3509/9120≈0.38476 and 13/29≈0.44828, and the maximal density is between 0.72 and 67/93≈0.72043. We also prove the lower bound 223/868≈0.25691 on the density of branching points in the tree of the ternary square-free language. Full article
(This article belongs to the Section Analysis of Algorithms and Complexity Theory)
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15 pages, 18352 KiB  
Article
Computation of Implicit Representation of Volumetric Shells with Predefined Thickness
by Martin Geier and Hussein Alihussein
Algorithms 2021, 14(4), 125; https://doi.org/10.3390/a14040125 - 18 Apr 2021
Cited by 2 | Viewed by 3068
Abstract
We propose and validate a method to find an implicit representation of a surface placed at a distance h from another implicit surface. With two such surfaces on either side of the original surface, a volumetric shell of predefined thickness can be obtained. [...] Read more.
We propose and validate a method to find an implicit representation of a surface placed at a distance h from another implicit surface. With two such surfaces on either side of the original surface, a volumetric shell of predefined thickness can be obtained. The usability of the proposed method is demonstrated through providing solid models of triply periodic minimal surface (TPMS) geometries with a predefined constant and variable thickness. The method has an adjustable order of convergence. If applied to surfaces with spatially varying thickness, the convergence order is limited to second order. This accuracy is still substantially higher than the accuracy of any contemporary 3D printer that could benefit from the function as an infill volume for shells with predefined thicknesses. Full article
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18 pages, 7589 KiB  
Article
A Two-Dimensional mKdV Linear Map and Its Application in Digital Image Cryptography
by La Zakaria, Endah Yuliani and Asmiati Asmiati
Algorithms 2021, 14(4), 124; https://doi.org/10.3390/a14040124 - 16 Apr 2021
Cited by 2 | Viewed by 2218
Abstract
Cryptography is the science and study of protecting data in computer and communication systems from unauthorized disclosure and modification. An ordinary difference equation (a map) can be used in encryption–decryption algorithms. In particular, the Arnold’s cat and the sine-Gordon linear maps can be [...] Read more.
Cryptography is the science and study of protecting data in computer and communication systems from unauthorized disclosure and modification. An ordinary difference equation (a map) can be used in encryption–decryption algorithms. In particular, the Arnold’s cat and the sine-Gordon linear maps can be used in cryptographic algorithms for encoding digital images. In this article, a two-dimensional linear mKdV map derived from an ordinary difference mKdV equation will be used in a cryptographic encoding algorithm. The proposed encoding algorithm will be compared with those generated using sine-Gordon and Arnold’s cat maps via the correlations between adjacent pixels in the encrypted image and the uniformity of the pixel distribution. Note that the mKdV map is derived from the partial discrete mKdV equation with Consistency Around the Cube (CAC) properties, whereas the sine-Gordon map is derived from the partial discrete sine-Gordon equation, which does not have CAC properties. Full article
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9 pages, 295 KiB  
Article
Estimating the Tour Length for the Close Enough Traveling Salesman Problem
by Debdatta Sinha Roy, Bruce Golden, Xingyin Wang and Edward Wasil
Algorithms 2021, 14(4), 123; https://doi.org/10.3390/a14040123 - 12 Apr 2021
Cited by 4 | Viewed by 3234
Abstract
We construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using [...] Read more.
We construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using as many as 14 independent variables on a set of 780 benchmark instances of the CETSP and compare the estimated tour lengths to the results from a Steiner zone heuristic. We validate our results on a new set of 234 instances that are similar to the 780 benchmark instances. We also generate results for a new set of 72 larger instances. Overall, our models fit the data well and do a very good job of estimating the tour length. In addition, we show that our modeling approach can be used to accurately estimate the optimal tour lengths for the CETSP. Full article
(This article belongs to the Special Issue Algorithms for Travelling Salesperson Problems)
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21 pages, 10092 KiB  
Article
Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering
by Fevrier Valdez, Oscar Castillo and Patricia Melin
Algorithms 2021, 14(4), 122; https://doi.org/10.3390/a14040122 - 12 Apr 2021
Cited by 33 | Viewed by 6197
Abstract
In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the [...] Read more.
In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms in Machine Learning)
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21 pages, 2031 KiB  
Article
Security Audit of a Blockchain-Based Industrial Application Platform
by Jan Stodt, Daniel Schönle, Christoph Reich, Fatemeh Ghovanlooy Ghajar, Dominik Welte and Axel Sikora
Algorithms 2021, 14(4), 121; https://doi.org/10.3390/a14040121 - 10 Apr 2021
Cited by 20 | Viewed by 4852
Abstract
In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through [...] Read more.
In recent years, both the Internet of Things (IoT) and blockchain technologies have been highly influential and revolutionary. IoT enables companies to embrace Industry 4.0, the Fourth Industrial Revolution, which benefits from communication and connectivity to reduce cost and to increase productivity through sensor-based autonomy. These automated systems can be further refined with smart contracts that are executed within a blockchain, thereby increasing transparency through continuous and indisputable logging. Ideally, the level of security for these IoT devices shall be very high, as they are specifically designed for this autonomous and networked environment. This paper discusses a use case of a company with legacy devices that wants to benefit from the features and functionality of blockchain technology. In particular, the implications of retrofit solutions are analyzed. The use of the BISS:4.0 platform is proposed as the underlying infrastructure. BISS:4.0 is intended to integrate the blockchain technologies into existing enterprise environments. Furthermore, a security analysis of IoT and blockchain present attacks and countermeasures are presented that are identified and applied to the mentioned use case. Full article
(This article belongs to the Special Issue Using Blockchain Technology in the Industry 4.0 Era: Cases and Applications)
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18 pages, 793 KiB  
Article
An Improved Artificial Bee Colony for Feature Selection in QSAR
by Yanhong Lin, Jing Wang, Xiaolin Li, Yuanzi Zhang and Shiguo Huang
Algorithms 2021, 14(4), 120; https://doi.org/10.3390/a14040120 - 9 Apr 2021
Cited by 7 | Viewed by 2677
Abstract
Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant [...] Read more.
Quantitative Structure–Activity Relationship (QSAR) aims to correlate molecular structure properties with corresponding bioactivity. Chance correlations and multicollinearity are two major problems often encountered when generating QSAR models. Feature selection can significantly improve the accuracy and interpretability of QSAR by removing redundant or irrelevant molecular descriptors. An artificial bee colony algorithm (ABC) that mimics the foraging behaviors of honey bee colony was originally proposed for continuous optimization problems. It has been applied to feature selection for classification but seldom for regression analysis and prediction. In this paper, a binary ABC algorithm is used to select features (molecular descriptors) in QSAR. Furthermore, we propose an improved ABC-based algorithm for feature selection in QSAR, namely ABC-PLS-1. Crossover and mutation operators are introduced to employed bee and onlooker bee phase to modify several dimensions of each solution, which not only saves the process of converting continuous values into discrete values, but also reduces the computational resources. In addition, a novel greedy selection strategy which selects the feature subsets with higher accuracy and fewer features helps the algorithm to converge fast. Three QSAR datasets are used for the evaluation of the proposed algorithm. Experimental results show that ABC-PLS-1 outperforms PSO-PLS, WS-PSO-PLS, and BFDE-PLS in accuracy, root mean square error, and the number of selected features. Moreover, we also study whether to implement scout bee phase when tracking regression problems and drawing such an interesting conclusion that the scout bee phase is redundant when dealing with the feature selection in low-dimensional and medium-dimensional regression problems. Full article
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12 pages, 510 KiB  
Article
Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis
by Qing Miao, Juhui Wei, Jiongqi Wang and Yuyun Chen
Algorithms 2021, 14(4), 119; https://doi.org/10.3390/a14040119 - 8 Apr 2021
Cited by 10 | Viewed by 2734
Abstract
Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the effective fault identification, is studied in this paper. Firstly, the stability and stability conditions [...] Read more.
Aiming at the problem of fault diagnosis in continuous time systems, a kind of fault diagnosis algorithm based on adaptive nonlinear proportional integral (PI) observer, which can realize the effective fault identification, is studied in this paper. Firstly, the stability and stability conditions of fault diagnosis method based on the PI observer are analyzed, and the upper bound of the fault estimation error is given. Secondly, the fault diagnosis algorithm based on adjustable nonlinear PI observer is designed and constructed, it is analyzed and we proved that the upper bound of fault estimation under this algorithm is better than that of the traditional method. Finally, the L-1011 unmanned aerial vehicle (UAV) is taken as the experimental object for numerical simulation, and the fault diagnosis method based on adaptive observer factor achieves faster response speed and more accurate fault identification results. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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30 pages, 326 KiB  
Article
Phase Congruential White Noise Generator
by Aleksei F. Deon, Oleg K. Karaduta and Yulian A. Menyaev
Algorithms 2021, 14(4), 118; https://doi.org/10.3390/a14040118 - 5 Apr 2021
Cited by 3 | Viewed by 6545
Abstract
White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal [...] Read more.
White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal generator with an improved matrix of autocorrelation coefficients. As a result, the generated signals of the white noise process have absolutely uniform intensities at the eigen Fourier frequencies. The simulation results confirm that the received signals have an adequate approximation of uniform white noise. Full article
(This article belongs to the Special Issue Algorithms in Stochastic Models)
18 pages, 1362 KiB  
Article
Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms
by Hirokazu Madokoro, Stephanie Nix and Kazuhito Sato
Algorithms 2021, 14(4), 117; https://doi.org/10.3390/a14040117 - 4 Apr 2021
Cited by 3 | Viewed by 2648
Abstract
This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior [...] Read more.
This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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8 pages, 288 KiB  
Article
Compressed Communication Complexity of Hamming Distance
by Shiori Mitsuya, Yuto Nakashima, Shunsuke Inenaga, Hideo Bannai and Masayuki Takeda
Algorithms 2021, 14(4), 116; https://doi.org/10.3390/a14040116 - 3 Apr 2021
Cited by 4 | Viewed by 2239
Abstract
We consider the communication complexity of the Hamming distance of two strings. Bille et al. [SPIRE 2018] considered the communication complexity of the longest common prefix (LCP) problem in the setting where the two parties have their strings in a compressed form, i.e., [...] Read more.
We consider the communication complexity of the Hamming distance of two strings. Bille et al. [SPIRE 2018] considered the communication complexity of the longest common prefix (LCP) problem in the setting where the two parties have their strings in a compressed form, i.e., represented by the Lempel-Ziv 77 factorization (LZ77) with/without self-references. We present a randomized public-coin protocol for a joint computation of the Hamming distance of two strings represented by LZ77 without self-references. Although our scheme is heavily based on Bille et al.’s LCP protocol, our complexity analysis is original which uses Crochemore’s C-factorization and Rytter’s AVL-grammar. As a byproduct, we also show that LZ77 with/without self-references are not monotonic in the sense that their sizes can increase by a factor of 4/3 when a prefix of the string is removed. Full article
(This article belongs to the Special Issue Algorithms and Data-Structures for Compressed Computation)
15 pages, 1049 KiB  
Article
Dynamic Initial Weight Assignment for MaxSAT
by Abdelraouf Ishtaiwi and Qasem Abu Al-Haija
Algorithms 2021, 14(4), 115; https://doi.org/10.3390/a14040115 - 31 Mar 2021
Viewed by 2294
Abstract
The Maximum Satisfiability (Maximum Satisfiability (MaxSAT)) approach is the choice, and perhaps the only one, to deal with most real-world problems as most of them are unsatisfiable. Thus, the search for a complete and consistent solution to a real-world problem is impractical due [...] Read more.
The Maximum Satisfiability (Maximum Satisfiability (MaxSAT)) approach is the choice, and perhaps the only one, to deal with most real-world problems as most of them are unsatisfiable. Thus, the search for a complete and consistent solution to a real-world problem is impractical due to computational and time constraints. As a result, MaxSAT problems and solving techniques are of exceptional interest in the domain of Satisfiability (Satisfiability (SAT)). Our research experimentally investigated the performance gains of extending the most recently developed SAT dynamic Initial Weight assignment technique (InitWeight) to handle the MaxSAT problems. Specifically, we first investigated the performance gains of dynamically assigning the initial weights in the Divide and Distribute Fixed Weights solver (DDFW+Initial Weight for Maximum Satisfiability (DDFW+InitMaxSAT)) over Divide and Distribute Fixed Weights solver (DDFW) when applied to solve a wide range of well-known unweighted MaxSAT problems obtained from DIMACS. Secondly, we compared DDFW+InitMaxSAT’s performance against three known state-of-the-art SAT solving techniques: YalSAT, ProbSAT, and Sparrow. We showed that the assignment of dynamic initial weights increased the performance of DDFW+InitMaxSAT against DDFW by an order of magnitude on the majority of problems and performed similarly otherwise. Furthermore, we showed that the performance of DDFW+InitMaxSAT was superior to the other state-of-the-art algorithms. Eventually, we showed that the InitWeight technique could be extended to handling partial MaxSAT with minor modifications. Full article
(This article belongs to the Section Randomized, Online, and Approximation Algorithms)
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11 pages, 6097 KiB  
Communication
Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5
by Margrit Kasper-Eulaers, Nico Hahn, Stian Berger, Tom Sebulonsen, Øystein Myrland and Per Egil Kummervold
Algorithms 2021, 14(4), 114; https://doi.org/10.3390/a14040114 - 31 Mar 2021
Cited by 145 | Viewed by 18158
Abstract
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 [...] Read more.
The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection. Full article
(This article belongs to the Special Issue Machine-Learning in Computer Vision Applications)
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45 pages, 6453 KiB  
Article
On a Robust and Efficient Numerical Scheme for the Simulation of Stationary 3-Component Systems with Non-Negative Species-Concentration with an Application to the Cu Deposition from a Cu-(β-alanine)-Electrolyte
by Stephan Daniel Schwoebel, Thomas Mehner and Thomas Lampke
Algorithms 2021, 14(4), 113; https://doi.org/10.3390/a14040113 - 30 Mar 2021
Cited by 2 | Viewed by 2884
Abstract
Three-component systems of diffusion–reaction equations play a central role in the modelling and simulation of chemical processes in engineering, electro-chemistry, physical chemistry, biology, population dynamics, etc. A major question in the simulation of three-component systems is how to guarantee non-negative species distributions in [...] Read more.
Three-component systems of diffusion–reaction equations play a central role in the modelling and simulation of chemical processes in engineering, electro-chemistry, physical chemistry, biology, population dynamics, etc. A major question in the simulation of three-component systems is how to guarantee non-negative species distributions in the model and how to calculate them effectively. Current numerical methods to enforce non-negative species distributions tend to be cost-intensive in terms of computation time and they are not robust for big rate constants of the considered reaction. In this article, a method, as a combination of homotopy methods, modern augmented Lagrangian methods, and adaptive FEMs is outlined to obtain a robust and efficient method to simulate diffusion–reaction models with non-negative concentrations. Although in this paper the convergence analysis is not described rigorously, multiple numerical examples as well as an application to elctro-deposition from an aqueous Cu2+-(β-alanine) electrolyte are presented. Full article
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18 pages, 688 KiB  
Article
An Effective Decomposition-Based Stochastic Algorithm for Solving the Permutation Flow-Shop Scheduling Problem
by Mehrdad Amirghasemi
Algorithms 2021, 14(4), 112; https://doi.org/10.3390/a14040112 - 30 Mar 2021
Cited by 6 | Viewed by 2893
Abstract
This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine [...] Read more.
This paper presents an effective stochastic algorithm that embeds a large neighborhood decomposition technique into a variable neighborhood search for solving the permutation flow-shop scheduling problem. The algorithm first constructs a permutation as a seed using a recursive application of the extended two-machine problem. In this method, the jobs are recursively decomposed into two separate groups, and, for each group, an optimal permutation is calculated based on the extended two-machine problem. Then the overall permutation, which is obtained by integrating the sub-solutions, is improved through the application of a variable neighborhood search technique. The same as the first technique, this one is also based on the decomposition paradigm and can find an optimal arrangement for a subset of jobs. In the employed large neighborhood search, the concept of the critical path has been used to help the decomposition process avoid unfruitful computation and arrange only promising contiguous parts of the permutation. In this fashion, the algorithm leaves those parts of the permutation which already have high-quality arrangements and concentrates on modifying other parts. The results of computational experiments on the benchmark instances indicate the procedure works effectively, demonstrating that solutions, in a very short distance of the best-known solutions, are calculated within seconds on a typical personal computer. In terms of the required running time to reach a high-quality solution, the procedure outperforms some well-known metaheuristic algorithms in the literature. Full article
(This article belongs to the Special Issue Stochastic Algorithms and Their Applications)
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17 pages, 503 KiB  
Article
Multiple Criteria Decision Making and Prospective Scenarios Model for Selection of Companies to Be Incubated
by Altina S. Oliveira, Carlos F. S. Gomes, Camilla T. Clarkson, Adriana M. Sanseverino, Mara R. S. Barcelos, Igor P. A. Costa and Marcos Santos
Algorithms 2021, 14(4), 111; https://doi.org/10.3390/a14040111 - 30 Mar 2021
Cited by 48 | Viewed by 4044
Abstract
This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the AHP-TOPSIS-2N Multiple Criteria Decision Making (MCDM) method to [...] Read more.
This paper proposes a model to evaluate business projects to get into an incubator, allowing to rank them in order of selection priority. The model combines the Momentum method to build prospective scenarios and the AHP-TOPSIS-2N Multiple Criteria Decision Making (MCDM) method to rank the alternatives. Six business projects were evaluated to be incubated. The Momentum method made it possible for us to create an initial core of criteria for the evaluation of incubation projects. The AHP-TOPSIS-2N method supported the decision to choose the company to be incubated by ranking the alternatives in order of relevance. Our evaluation model has improved the existing models used by incubators. This model can be used and/or adapted by any incubator to evaluate the business projects to be incubated. The set of criteria for the evaluation of incubation projects is original and the use of prospective scenarios with an MCDM method to evaluate companies to be incubated does not exist in the literature. Full article
(This article belongs to the Special Issue Algorithms and Models for Dynamic Multiple Criteria Decision Making)
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10 pages, 552 KiB  
Article
Arc-Completion of 2-Colored Best Match Graphs to Binary-Explainable Best Match Graphs
by David Schaller, Manuela Geiß, Marc Hellmuth and Peter F. Stadler
Algorithms 2021, 14(4), 110; https://doi.org/10.3390/a14040110 - 29 Mar 2021
Cited by 2 | Viewed by 2148
Abstract
Best match graphs (BMGs) are vertex-colored digraphs that naturally arise in mathematical phylogenetics to formalize the notion of evolutionary closest genes w.r.t. an a priori unknown phylogenetic tree. BMGs are explained by unique least resolved trees. We prove that the property of a [...] Read more.
Best match graphs (BMGs) are vertex-colored digraphs that naturally arise in mathematical phylogenetics to formalize the notion of evolutionary closest genes w.r.t. an a priori unknown phylogenetic tree. BMGs are explained by unique least resolved trees. We prove that the property of a rooted, leaf-colored tree to be least resolved for some BMG is preserved by the contraction of inner edges. For the special case of two-colored BMGs, this leads to a characterization of the least resolved trees (LRTs) of binary-explainable trees and a simple, polynomial-time algorithm for the minimum cardinality completion of the arc set of a BMG to reach a BMG that can be explained by a binary tree. Full article
(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)
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11 pages, 814 KiB  
Article
Median Filter Aided CNN Based Image Denoising: An Ensemble Approach
by Subhrajit Dey, Rajdeep Bhattacharya, Friedhelm Schwenker and Ram Sarkar
Algorithms 2021, 14(4), 109; https://doi.org/10.3390/a14040109 - 28 Mar 2021
Cited by 14 | Viewed by 4757
Abstract
Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose [...] Read more.
Image denoising is a challenging research problem that aims to recover noise-free images from those that are contaminated with noise. In this paper, we focus on the denoising of images that are contaminated with additive white Gaussian noise. For this purpose, we propose an ensemble learning model that uses the output of three image denoising models, namely ADNet, IRCNN, and DnCNN, in the ratio of 2:3:6, respectively. The first model (ADNet) consists of Convolutional Neural Networks with attention along with median filter layers after every convolutional layer and a dilation rate of 8. In the case of the second model, it is a feed forward denoising CNN or DnCNN with median filter layers after half of the convolutional layers. For the third model, which is Deep CNN Denoiser Prior or IRCNN, the model contains dilated convolutional layers and median filter layers up to the dilated convolutional layers with a dilation rate of 6. By quantitative analysis, we note that our model performs significantly well when tested on the BSD500 and Set12 datasets. Full article
(This article belongs to the Special Issue Mathematical Models and Their Applications II)
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15 pages, 1823 KiB  
Article
Research on Optimization of Multi-Objective Regional Public Transportation Scheduling
by Xinfeng Yang and Yicheng Qi
Algorithms 2021, 14(4), 108; https://doi.org/10.3390/a14040108 - 28 Mar 2021
Cited by 6 | Viewed by 2802
Abstract
The optimization of bus scheduling is a key method to improve bus service. So, the purpose of this paper is to address the regional public transportation dispatching problem, while taking into account the association between the departure time of buses and the waiting [...] Read more.
The optimization of bus scheduling is a key method to improve bus service. So, the purpose of this paper is to address the regional public transportation dispatching problem, while taking into account the association between the departure time of buses and the waiting time of passengers. A bi-objective optimization model for regional public transportation scheduling is established to minimize the total waiting cost of passengers and to maximize the comprehensive service rate of buses. Moreover, a NSGA-II algorithm with adaptive adjusted model for crossover and mutation probability is designed to obtain the Pareto solution set of this problem, and the entropy weight-TOPSIS method is utilized to make a decision. Then the algorithms are compared with examples, and the results show that the model is feasible, and the proposed algorithms are achievable in solving the regional public transportation scheduling problem. Full article
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9 pages, 536 KiB  
Article
Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16
by Pengchang Xu, Jiaxiang Zhao and Jie Zhang
Algorithms 2021, 14(4), 107; https://doi.org/10.3390/a14040107 - 28 Mar 2021
Cited by 12 | Viewed by 2621
Abstract
The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known [...] Read more.
The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. The MCC value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these MCC values of our trained deep neural network exceed the corresponding values of existing prediction methods. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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17 pages, 3167 KiB  
Article
Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
by Petr Němec and Petr Stodola
Algorithms 2021, 14(4), 106; https://doi.org/10.3390/a14040106 - 26 Mar 2021
Cited by 8 | Viewed by 4948
Abstract
Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle [...] Read more.
Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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14 pages, 344 KiB  
Article
A Quasi-Hole Detection Algorithm for Recognizing k-Distance-Hereditary Graphs, with k < 2
by Serafino Cicerone
Algorithms 2021, 14(4), 105; https://doi.org/10.3390/a14040105 - 25 Mar 2021
Cited by 2 | Viewed by 2203
Abstract
Cicerone and Di Stefano defined and studied the class of k-distance-hereditary graphs, i.e., graphs where the distance in each connected induced subgraph is at most k times the distance in the whole graph. The defined graphs represent a generalization of the well [...] Read more.
Cicerone and Di Stefano defined and studied the class of k-distance-hereditary graphs, i.e., graphs where the distance in each connected induced subgraph is at most k times the distance in the whole graph. The defined graphs represent a generalization of the well known distance-hereditary graphs, which actually correspond to 1-distance-hereditary graphs. In this paper we make a step forward in the study of these new graphs by providing characterizations for the class of all the k-distance-hereditary graphs such that k<2. The new characterizations are given in terms of both forbidden subgraphs and cycle-chord properties. Such results also lead to devise a polynomial-time recognition algorithm for this kind of graph that, according to the provided characterizations, simply detects the presence of quasi-holes in any given graph. Full article
(This article belongs to the Special Issue Graph Algorithms and Applications)
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21 pages, 4274 KiB  
Article
Ontology Based Governance for Employee Services
by Eleftherios Tzagkarakis, Haridimos Kondylakis, George Vardakis and Nikolaos Papadakis
Algorithms 2021, 14(4), 104; https://doi.org/10.3390/a14040104 - 25 Mar 2021
Cited by 9 | Viewed by 3143
Abstract
Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indifferent. Public administration is evolving and taking on a new form through e-government. A large number of [...] Read more.
Advances in computers and communications have significantly changed almost every aspect of our daily activity. In this maze of change, governments around the world cannot remain indifferent. Public administration is evolving and taking on a new form through e-government. A large number of organizations have set up websites, establishing an online interface with the citizens and businesses with which it interacts. However, most organizations, especially the decentralized agencies of the ministries and local authorities, do not offer their information electronically despite the fact that they provide many information services that are not integrated with other e-government services. Besides, these services are mainly focused on serving citizens and businesses and less on providing services to employees. In this paper, we describe the process of developing an ontology to support the administrative procedures of decentralized government organizations. Finally, we describe the development of an e-government portal that provides employees services that are processed online, using the above ontology for modeling and data management. Full article
(This article belongs to the Special Issue Ontologies, Ontology Development and Evaluation)
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16 pages, 296 KiB  
Article
Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems
by Eirini Georgoulaki, Kostas Kollias and Tami Tamir
Algorithms 2021, 14(4), 103; https://doi.org/10.3390/a14040103 - 25 Mar 2021
Cited by 4 | Viewed by 2315
Abstract
We study cost-sharing games in real-time scheduling systems where the server’s activation cost in every time slot is a function of its load. We focus on monomial cost functions and consider both the case when the degree is less than one (inducing positive [...] Read more.
We study cost-sharing games in real-time scheduling systems where the server’s activation cost in every time slot is a function of its load. We focus on monomial cost functions and consider both the case when the degree is less than one (inducing positive congestion effect for the jobs) and when it is greater than one (inducing negative congestion effect for the jobs). For the former case, we provide tight bounds on the price of anarchy, and show that the price of anarchy grows to infinity as a polynomial of the number of jobs in the game. For the latter, we observe that existing results provide constant and tight (asymptotically in the degree of the monomial) bounds on the price of anarchy. We then turn to analyze payment mechanism with arbitrary cost-sharing, that is, when the strategy of a player includes also its payment. We show that our mechanism reduces the price of anarchy of games with n jobs and unit server costs from Θ(n) to 2. We also show that, for a restricted class of instances, a similar improvement is achieved for monomial server costs. This is not the case, however, for unrestricted instances of monomial costs, for which we prove that the price of anarchy remains super-constant for our mechanism. For systems with load-independent activation costs, we show that our mechanism can produce an optimal solution which is stable against coordinated deviations. Full article
(This article belongs to the Special Issue Algorithmic Game Theory 2020)
15 pages, 1453 KiB  
Article
Prediction of Intrinsically Disordered Proteins Using Machine Learning Algorithms Based on Fuzzy Entropy Feature
by Lin Zhang, Haiyuan Liu and Hao He
Algorithms 2021, 14(4), 102; https://doi.org/10.3390/a14040102 - 24 Mar 2021
Cited by 2 | Viewed by 2292
Abstract
We used fuzzy entropy as a feature to optimize the intrinsically disordered protein prediction scheme. The optimization scheme requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of [...] Read more.
We used fuzzy entropy as a feature to optimize the intrinsically disordered protein prediction scheme. The optimization scheme requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of two propensities. Notably, this is the first time that fuzzy entropy has been applied to the field of protein sequencing. In addition, we used three machine learning to examine the prediction results before and after optimization. The results show that the use of fuzzy entropy leads to an improvement in the performance of different algorithms, demonstrating the generality of its application. Finally, we compare the simulation results of our scheme with those of some existing schemes to demonstrate its effectiveness. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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24 pages, 1967 KiB  
Article
Chaos and Stability in a New Iterative Family for Solving Nonlinear Equations
by Alicia Cordero, Marlon Moscoso-Martínez and Juan R. Torregrosa
Algorithms 2021, 14(4), 101; https://doi.org/10.3390/a14040101 - 24 Mar 2021
Cited by 16 | Viewed by 2749
Abstract
In this paper, we present a new parametric family of three-step iterative for solving nonlinear equations. First, we design a fourth-order triparametric family that, by holding only one of its parameters, we get to accelerate its convergence and finally obtain a sixth-order uniparametric [...] Read more.
In this paper, we present a new parametric family of three-step iterative for solving nonlinear equations. First, we design a fourth-order triparametric family that, by holding only one of its parameters, we get to accelerate its convergence and finally obtain a sixth-order uniparametric family. With this last family, we study its convergence, its complex dynamics (stability), and its numerical behavior. The parameter spaces and dynamical planes are presented showing the complexity of the family. From the parameter spaces, we have been able to determine different members of the family that have bad convergence properties, as attracting periodic orbits and attracting strange fixed points appear in their dynamical planes. Moreover, this same study has allowed us to detect family members with especially stable behavior and suitable for solving practical problems. Several numerical tests are performed to illustrate the efficiency and stability of the presented family. Full article
(This article belongs to the Special Issue 2021 Selected Papers from Algorithms Editorial Board Members)
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