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Algorithms
Special Issues
Hybrid Intelligent Algorithms
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Hybrid Intelligent Algorithms

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 12538

Special Issue Editors

Prof. Dr. Grigorios Beligiannis

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Guest Editor
Department of Food Science & Technology, University of Patras, Agrinio Campus, G. Seferi 2, 30100 Agrinio, Greece
Interests: artificial intelligence; computational intelligence; machine learning; genetic/evolutionary algorithms; decision support theory; intelligent information systems; applications of hybrid intelligent information systems for modeling real world time series belonging to linear and non-linear systems; design and development of hybrid intelligent algorithms for solving timetabling and scheduling problems; multi-objective optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Efstratios F. Georgopoulos

E-Mail Website
Guest Editor
Technological Educational Institute (T.E.I.) of Peloponnese, 24100 Kalamata, Greece
Interests: Computational Intelligence; Artificial Neural Networks; Evolutionary Algorithms; Machine Learning; Data Analysis; Mathematical Modelling; System Modelling; Signals and Systems; Time Series; Computational Finance
Prof. Dr. Spiridon D. Likothanassis

E-Mail Website
Guest Editor
Department of Computer Engineering & Informatics, University of Patras, ΤΚ 26500 Patras, Greece
Interests: computational and artificial intelligence; intelligent agent systems; computational biology and bioinformatics; knowledge management; cloud computing and big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Isidoros Perikos

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Guest Editor
Department of Computer Engineering & Informatics, University of Patras, 26504 Rio, Greece
Interests: artificial intelligence; learning technologies; hybrid systems; natural language processing; virtual reality
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis X. Tassopoulos

E-Mail Website
Guest Editor
Department of Business Administration of Food and Agricultural Enterprises, University of Patras, 30100 Agrinio, Greece
Interests: Artificial Intelligence; Computational Intelligence; Intelligent Information Systems

Special Issue Information

Dear Colleagues,

Combining intelligent algorithms coming from different computational intelligence areas to solve difficult real world problems, and especially their hybridization, has become very popular in recent decades. This is mainly due to the growing awareness that the application of hybrid intelligent algorithms results most of the time to better performance than applying individual computational intelligence algorithms, such as neural networks, evolutionary algorithms, fuzzy systems, particle swarm optimization, etc. The application of such hybrid intelligent schemes has indicated that hybrid intelligence algorithms succeed in solving some very difficult real world problems in which the application of deterministic or individual computational intelligence algorithms is either not possible or extremely time-consuming. In a hybrid intelligence system, a synergistic combination of multiple intelligent techniques is used to build an efficient solution to deal effectively with a particular problem. This Special Issue will comprise papers focused on hybrid intelligent algorithms following different approaches and their real world applications.

Dr. Grigorios N. Beligiannis
Prof. Dr. Spiridon Likothannassis
Dr. Ioannis X. Tassopoulos
Dr. Efstratios F. Georgopoulos
Dr. Isidoros Perikos
Guest Editor

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.

Published Papers (6 papers)

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Research

17 pages, 3936 KiB  
Article
Security and Ownership in User-Defined Data Meshes
by Michalis Pingos, Panayiotis Christodoulou and Andreas S. Andreou
Algorithms 2024, 17(4), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/a17040169 - 22 Apr 2024
Viewed by 299
Abstract
Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, [...] Read more.
Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of data meshes and data products in response to user requests, ensuring that stakeholders have access to specific portions of the data mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates the secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of data meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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20 pages, 1984 KiB  
Article
Deep-Shallow Metaclassifier with Synthetic Minority Oversampling for Anomaly Detection in a Time Series
by MohammadHossein Reshadi, Wen Li, Wenjie Xu, Precious Omashor, Albert Dinh, Scott Dick, Yuntong She and Michael Lipsett
Algorithms 2024, 17(3), 114; https://0-doi-org.brum.beds.ac.uk/10.3390/a17030114 - 10 Mar 2024
Viewed by 910
Abstract
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow [...] Read more.
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hybrid anomaly detectors (a “normal model” followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector’s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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14 pages, 2882 KiB  
Article
Agreement, Accuracy, and Reliability of a New Algorithm for the Detection of Change of Direction Angle Based on Integrating Inertial Data from Inertial Sensors
by Roberto Avilés, Diego Brito de Souza, José Pino-Ortega and Julen Castellano
Algorithms 2023, 16(11), 496; https://0-doi-org.brum.beds.ac.uk/10.3390/a16110496 - 25 Oct 2023
Viewed by 1123
Abstract
The development of algorithms applied to new technologies allows a better understanding of many of the movements in team sports. The purpose of this work was to analyze the validity, precision, and reproducibility of an algorithm to detect angulation of changes of direction [...] Read more.
The development of algorithms applied to new technologies allows a better understanding of many of the movements in team sports. The purpose of this work was to analyze the validity, precision, and reproducibility of an algorithm to detect angulation of changes of direction (CoDs) while running, of between 45° and 180°, both to the left and the right at different speeds, in a standardized context. For this, five participants performed a total of 200 CoDs at 13 km/h and 128 CoDs at 18 km/h while wearing three inertial sensors. The information obtained from the sensors was contrasted with observation and coding using high-resolution video. Agreement between systems was assessed using Bland–Altman 95% limits of agreement as well as effect size (ES) and % difference between means. Reproducibility was evaluated using the standard error (CV%). The algorithm overestimated the angulation of 90° and 135° to the right (Cohen’s d > 0.91). The algorithm showed high precision when the angulations recorded at 13 km/h and 18 km/h were compared, except at 45° to the left (mean bias = −2.6°; Cohen’s d = −0.57). All angulations showed excellent reproducibility (CV < 5%) except at 45° (CV = 11%), which worsened when the pre-CoD speed was 18 km/h (CV < 16%). The algorithm showed a high degree of validity and reproducibility to detect angles during CoDs. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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30 pages, 795 KiB  
Article
A Hybrid Discrete Memetic Algorithm for Solving Flow-Shop Scheduling Problems
by Levente Fazekas, Boldizsár Tüű-Szabó, László T. Kóczy, Olivér Hornyák and Károly Nehéz
Algorithms 2023, 16(9), 406; https://0-doi-org.brum.beds.ac.uk/10.3390/a16090406 - 26 Aug 2023
Viewed by 1638
Abstract
Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptional cases, there are no explicit algorithms for finding the [...] Read more.
Flow-shop scheduling problems are classic examples of multi-resource and multi-operation scheduling problems where the objective is to minimize the makespan. Because of the high complexity and intractability of the problem, apart from some exceptional cases, there are no explicit algorithms for finding the optimal permutation in multi-machine environments. Therefore, different heuristic approaches, including evolutionary and memetic algorithms, are used to obtain the solution—or at least, a close enough approximation of the optimum. This paper proposes a novel approach: a novel combination of two rather efficient such heuristics, the discrete bacterial memetic evolutionary algorithm (DBMEA) proposed earlier by our group, and a conveniently modified heuristics, the Monte Carlo tree method. By their nested combination a new algorithm was obtained: the hybrid discrete bacterial memetic evolutionary algorithm (HDBMEA), which was extensively tested on the Taillard benchmark data set. Our results have been compared against all important other approaches published in the literature, and we found that this novel compound method produces good results overall and, in some cases, even better approximations of the optimum than any of the so far proposed solutions. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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27 pages, 1909 KiB  
Article
Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
by Iosif V. Katsaragakis, Ioannis X. Tassopoulos and Grigorios N. Beligiannis
Algorithms 2020, 13(9), 223; https://0-doi-org.brum.beds.ac.uk/10.3390/a13090223 - 06 Sep 2020
Cited by 11 | Viewed by 3391
Abstract
Presented in this research paper is an attempt to apply a cat swarm optimization (CSO)-based algorithm to the urban transit routing problem (UTRP). Using the proposed algorithm, we can attain feasible and efficient (near) optimal route sets for public transportation networks. It is, [...] Read more.
Presented in this research paper is an attempt to apply a cat swarm optimization (CSO)-based algorithm to the urban transit routing problem (UTRP). Using the proposed algorithm, we can attain feasible and efficient (near) optimal route sets for public transportation networks. It is, to our knowledge, the first time that cat swarm optimization (CSO)-based algorithm is applied to cope with this specific problem. The algorithm’s efficiency and excellent performance are demonstrated by conducting experiments with both real-world as well as artificial data. These specific data have also been used as test instances by other researchers in their publications. Computational results reveal that the proposed cat swarm optimization (CSO)-based algorithm exhibits better performance, using the same evaluation criteria, compared to most of the other existing approaches applied to the same test instances. The differences of the proposed algorithm in comparison with other published approaches lie in its main process, which is a modification of the classic cat swarm optimization (CSO) algorithm applied to solve the urban transit routing problem. This modification in addition to a variation of the initialization process, as well as the enrichment of the algorithm with a process of improving the final solution, constitute the innovations of this contribution. The UTRP is studied from both passenger and provider sides of interest, and the algorithm is applied in both cases according to necessary modifications. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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20 pages, 4353 KiB  
Article
Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine
by Chong Zhou, Shengjie Li, Yuhe Zhang, Zhikun Chen and Cuijun Zhang
Algorithms 2019, 12(11), 225; https://0-doi-org.brum.beds.ac.uk/10.3390/a12110225 - 28 Oct 2019
Viewed by 3255
Abstract
Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a [...] Read more.
Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms. Full article
(This article belongs to the Special Issue Hybrid Intelligent Algorithms)
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