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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (30 August 2022) | Viewed by 24383

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Prof. Dr. Eugene Semenkin

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Department of System Analysis and Operations Research, Siberian Institute of Applied System Analysis, Reshetnev Siberian State University of Science and Technology, 41031 Krasnoyarsk, Russia
Interests: modeling and optimization of complicated systems; computational intelligence; evolutionary algorithms; artificial intelligence; data mining
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Prof. Dr. Todor Ganchev

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Department of Computer Science and Engineering, Technical University of Varna, 9010 Varna, Bulgaria
Interests: emotion recognition; speech and audio processing; bioacoustics; biometrics; physiological signal processing
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Prof. Dr. Predrag S. Stanimirovic

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Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18106 Niš, Serbia
Interests: numerical linear algebra; operations research; nonlinear optimization; heuristic optimization; hybrid methods of optimization; gradient neural networks; zeroing neural networks; symbolic computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current Special Issue “Mathematical Models and Their Applications II” of Algorithms is intended as an international forum for the presentation of original mathematical modeling results for software and hardware applications in various fields. It aims to stimulate lively discussion among researchers as well as industrialists.

Papers may discuss theories, applications, evaluation, limitations, general tools, and techniques. Discussion papers that critically evaluate approaches or processing strategies and prototype demonstrations are especially welcome.

The Special Issue will cover a broad range of research topics, including but not limited to:

Mathematical models and their applications;
Mathematical modeling techniques;
Optimization techniques, including multicriterion optimization and decision-making support;
Data mining and knowledge discovery;
Machine learning;
Pattern recognition;
Learning in evolutionary algorithms;
Genetic programming;
Artificial neural networks;
Computational intelligence and its applications;
Bio-inspired and swarm intelligence;
Text/web/data mining;
Human–computer interaction;
Natural language processing;
Applications in engineering, natural sciences, social sciences, and computer science.

Prof. Dr. Eugene Semenkin
Prof. Dr. Todor Ganchev
Prof. Dr. Predrag Stanimirović
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Mathematical modeling
Optimization
Machine learning
Data mining
Computational intelligence
Applications

Related Special Issues

Mathematical Models and Their Applications in Algorithms (5 articles)
Mathematical Models and Their Applications II in Algorithms (5 articles)

Published Papers (10 papers)

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Research

Jump to: Review

20 pages, 6900 KiB

Open AccessArticle

A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems

by Aleksei Vakhnin and Evgenii Sopov

Algorithms 2022, 15(12), 451; https://doi.org/10.3390/a15120451 - 29 Nov 2022

Cited by 1 | Viewed by 1255

Abstract

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process (“on fly”) from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC’2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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13 pages, 2151 KiB

Open AccessArticle

Modifications of Flower Pollination, Teacher-Learner and Firefly Algorithms for Solving Multiextremal Optimization Problems

by Pavel Sorokovikov and Alexander Gornov

Algorithms 2022, 15(10), 359; https://doi.org/10.3390/a15100359 - 28 Sep 2022

Viewed by 1003

Abstract

The article offers a possible treatment for the numerical research of tasks which require searching for an absolute optimum. This approach is established by employing both globalized nature-inspired methods as well as local descent methods for exploration and exploitation. Three hybrid nonconvex minimization algorithms are developed and implemented. Modifications of flower pollination, teacher-learner, and firefly algorithms are used as nature-inspired methods for global searching. The modified trust region method based on the main diagonal approximation of the Hessian matrix is applied for local refinement. We have performed the numerical comparison of variants of the realized approach employing a representative collection of multimodal objective functions. The implemented nonconvex optimization methods have been used to solve the applied problems. These tasks utilize an optimization of the low-energy metal Sutton-Chen clusters potentials with a very large number of atoms and the parametric identification of the nonlinear dynamic model. The results of this research confirms the performance of the suggested algorithms. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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14 pages, 2137 KiB

Open AccessArticle

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

by Tatiana Karaseva and Eugene Semenkin

Algorithms 2022, 15(10), 351; https://doi.org/10.3390/a15100351 - 27 Sep 2022

Viewed by 1458

Abstract

Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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25 pages, 1144 KiB

Open AccessArticle

A Hybrid Clustering Approach Based on Fuzzy Logic and Evolutionary Computation for Anomaly Detection

by Shakhnaz Akhmedova, Vladimir Stanovov and Yukihiro Kamiya

Algorithms 2022, 15(10), 342; https://doi.org/10.3390/a15100342 - 22 Sep 2022

Cited by 4 | Viewed by 1604

Abstract

In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initially use a portion of labelled data from known classes to divide them into a given number of clusters, and then attempt to determine if the new instances, which may be unlabelled, belong to the known or novel classes or if they are anomalies, namely if they are extreme values that deviate from other observations, indicating noise or errors in measurement. However, for each class in HPFuzzNDA clusters are designed by using the new evolutionary algorithm NL-SHADE-RSP, the latter is a modification of the well-known L-SHADE approach. Additionally, the number of clusters for all classes is automatically adjusted in each step of HPFuzzNDA to improve its efficiency. The performance of the HPFuzzNDA approach was evaluated on a set of benchmark problems, specifically generated for novelty and anomaly detection. Experimental results demonstrated the workability and usefulness of the proposed approach as it was able to detect extensions of the known classes and to find new classes in addition to the anomalies. Moreover, numerical results showed that it outperformed PFuzzND. This was exhibited by the new mechanism proposed for cluster adjustments allowing HPFuzzNDA to achieve better classification accuracy in addition to better results in terms of macro F-score metric. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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20 pages, 3066 KiB

Open AccessArticle

Classification of Program Texts Represented as Markov Chains with Biology-Inspired Algorithms-Enhanced Extreme Learning Machines

by Liliya A. Demidova and Artyom V. Gorchakov

Algorithms 2022, 15(9), 329; https://doi.org/10.3390/a15090329 - 15 Sep 2022

Cited by 5 | Viewed by 1971

Abstract

The massive nature of modern university programming courses increases the burden on academic workers. The Digital Teaching Assistant (DTA) system addresses this issue by automating unique programming exercise generation and checking, and provides means for analyzing programs received from students by the end of semester. In this paper, we propose a machine learning-based approach to the classification of student programs represented as Markov chains. The proposed approach enables real-time student submissions analysis in the DTA system. We compare the performance of different multi-class classification algorithms, such as support vector machine (SVM), the k nearest neighbors (KNN) algorithm, random forest (RF), and extreme learning machine (ELM). ELM is a single-hidden layer feedforward network (SLFN) learning scheme that drastically speeds up the SLFN training process. This is achieved by randomly initializing weights of connections among input and hidden neurons, and explicitly computing weights of connections among hidden and output neurons. The experimental results show that ELM is the most computationally efficient algorithm among the considered ones. In addition, we apply biology-inspired algorithms to ELM input weights fine-tuning in order to further improve the generalization capabilities of this algorithm. The obtained results show that ELMs fine-tuned with biology-inspired algorithms achieve the best accuracy on test data in most of the considered problems. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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21 pages, 370 KiB

Open AccessArticle

Dark Type Dynamical Systems: The Integrability Algorithm and Applications

by Yarema A. Prykarpatsky, Ilona Urbaniak, Radosław A. Kycia and Anatolij K. Prykarpatski

Algorithms 2022, 15(8), 266; https://doi.org/10.3390/a15080266 - 28 Jul 2022

Cited by 4 | Viewed by 1209

Abstract

Based on a devised gradient-holonomic integrability testing algorithm, we analyze a class of dark type nonlinear dynamical systems on spatially one-dimensional functional manifolds possessing hidden symmetry properties and allowing their linearization on the associated cotangent spaces. We described main spectral properties of nonlinear Lax type integrable dynamical systems on periodic functional manifolds particular within the classical Floquet theory, as well as we presented the determining functional relationships between the conserved quantities and related geometric Poisson and recursion structures on functional manifolds. For evolution flows on functional manifolds, parametrically depending on additional functional variables, naturally related with the classical Bellman-Pontriagin optimal control problem theory, we studied a wide class of nonlinear dynamical systems of dark type on spatially one-dimensional functional manifolds, which are both of diffusion and dispersion classes and can have interesting applications in modern physics, optics, mechanics, hydrodynamics and biology sciences. We prove that all of these dynamical systems possess rich hidden symmetry properties, are Lax type linearizable and possess finite or infinite hierarchies of suitably ordered conserved quantities. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

25 pages, 3969 KiB

Open AccessArticle

Eyes versus Eyebrows: A Comprehensive Evaluation Using the Multiscale Analysis and Curvature-Based Combination Methods in Partial Face Recognition

by Regina Lionnie, Catur Apriono and Dadang Gunawan

Algorithms 2022, 15(6), 208; https://doi.org/10.3390/a15060208 - 14 Jun 2022

Cited by 4 | Viewed by 1942

Abstract

This work aimed to find the most discriminative facial regions between the eyes and eyebrows for periocular biometric features in a partial face recognition system. We propose multiscale analysis methods combined with curvature-based methods. The goal of this combination was to capture the details of these features at finer scales and offer them in-depth characteristics using curvature. The eye and eyebrow images cropped from four face 2D image datasets were evaluated. The recognition performance was calculated using the nearest neighbor and support vector machine classifiers. Our proposed method successfully produced richer details in finer scales, yielding high recognition performance. The highest accuracy results were 76.04% and 98.61% for the limited dataset and 96.88% and 93.22% for the larger dataset for the eye and eyebrow images, respectively. Moreover, we compared the results between our proposed methods and other works, and we achieved similar high accuracy results using only eye and eyebrow images. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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19 pages, 2397 KiB

Open AccessArticle

Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments

by Vladimir Stanovov, Shakhnaz Akhmedova, Aleksei Vakhnin, Evgenii Sopov, Eugene Semenkin and Michael Affenzeller

Algorithms 2022, 15(5), 154; https://doi.org/10.3390/a15050154 - 30 Apr 2022

Cited by 4 | Viewed by 1988

Abstract

In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that applying the mutation operator from differential evolution to the personal best positions of the particles allows for improving the algorithm performance. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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16 pages, 1096 KiB

Open AccessArticle

The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing

by Nicola Ognibene Pietri, Xiaochen Chou, Dominic Loske, Matthias Klumpp and Roberto Montemanni

Algorithms 2021, 14(12), 350; https://doi.org/10.3390/a14120350 - 30 Nov 2021

Cited by 8 | Viewed by 3409

Abstract

Online shopping is growing fast due to the increasingly widespread use of digital services. During the COVID-19 pandemic, the desire for contactless shopping has further changed consumer behavior and accelerated the acceptance of online grocery purchases. Consequently, traditional brick-and-mortar retailers are developing omnichannel solutions such as click-and-collect services to fulfill the increasing demand. In this work, we consider the Buy-Online-Pick-up-in-Store concept, in which online orders are collected by employees of the conventional stores. As labor is a major cost driver, we apply and discuss different optimizing strategies in the picking and packing process based on real-world data from a German retailer. With comparison of different methods, we estimate the improvements in efficiency in terms of time spent during the picking process. Additionally, the time spent on the packing process can be further decreased by applying a mathematical model that guides the employees on how to organize the articles in different shopping bags during the picking process. In general, we put forward effective strategies for the Buy-Online-Pick-up-in-Store paradigm that can be easily implemented by stores with different topologies. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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Review

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19 pages, 5851 KiB

Open AccessEditor’s ChoiceReview

Machine Learning in Cereal Crops Disease Detection: A Review

by Fraol Gelana Waldamichael, Taye Girma Debelee, Friedhelm Schwenker, Yehualashet Megersa Ayano and Samuel Rahimeto Kebede

Algorithms 2022, 15(3), 75; https://doi.org/10.3390/a15030075 - 24 Feb 2022

Cited by 19 | Viewed by 6967

Abstract

Cereals are an important and major source of the human diet. They constitute more than two-thirds of the world’s food source and cover more than 56% of the world’s cultivatable land. These important sources of food are affected by a variety of damaging diseases, causing significant loss in annual production. In this regard, detection of diseases at an early stage and quantification of the severity has acquired the urgent attention of researchers worldwide. One emerging and popular approach for this task is the utilization of machine learning techniques. In this work, we have identified the most common and damaging diseases affecting cereal crop production, and we also reviewed 45 works performed on the detection and classification of various diseases that occur on six cereal crops within the past five years. In addition, we identified and summarised numerous publicly available datasets for each cereal crop, which the lack thereof we identified as the main challenges faced for researching the application of machine learning in cereal crop detection. In this survey, we identified deep convolutional neural networks trained on hyperspectral data as the most effective approach for early detection of diseases and transfer learning as the most commonly used and yielding the best result training method. Full article

(This article belongs to the Special Issue Mathematical Models and Their Applications III)

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