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Algorithms
Special Issues
Recent Advances in Algorithms for Swarm Systems
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Recent Advances in Algorithms for Swarm Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 1 March 2025 | Viewed by 4130

Special Issue Editors

Prof. Dr. Jesús Ángel Román Gallego

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Guest Editor
Escuela Politécnica Superior de Zamora, University of Salamanca, Av. Requejo 33, C.P. 49022 Zamora, Spain
Interests: swarm systems; artificial intelligence; computer engineering; service-oriented architectures; expert systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Escuadra Burrieza

E-Mail Website
Guest Editor
Computer Science and Automation Department, University of Salamanca, Av. Requejo 33, C.P. 49022 Zamora, Spain
Interests: algorithms optimization; machine learning; expert systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Manuel Martín-Merino

E-Mail Website
Guest Editor
Faculty of Informatics, Universidad Pontificia de Salamanca, 5-37002 Salamanca, Spain
Interests: bioinformatics; artificial intelligence; pattern recognition; machine learning algorithms; neural networks; data mining
Dr. Andres Iglesias

E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: swarm intelligence and swarm robotics; bio-inspired optimization; computer graphics; geometric modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Within artificial intelligence, swarm systems are computational systems implemented from algorithms which offer coordination and interaction among its elements to achieve a common goal. These algorithmic systems are inspired by the behavior of different groups of animals, such as ant and bee colonies, wolf packs, and other animals when they work together to achieve complex objectives.

The application of algorithms that implement this type of systems is varied. The fields of application include:

Optimization: these algorithms are used to solve optimization problems in areas, such as transportation, logistics, engineering, etc.

Robotics: these algorithms are used in the coordination of sets of robots to achieve global objectives. They are also used in defense systems for surveillance with drones and autonomous robots.

Sensors: these algorithms are used to obtain joint data from different sensors.

This Special Issue is open for contributions containing latest research in algorithmic applications in swarm systems.

Prof. Dr. Jesús Ángel Román Gallego
Prof. Dr. José Escuadra Burrieza
Prof. Dr. Manuel Martín-Merino
Dr. Andres Iglesias
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

  • swarm systems
  • artificial intelligence
  • optimization algorithms

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Published Papers (2 papers)

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29 pages, 767 KiB  
Article
Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
by Hossein Zolfagharinia, Mehdi Najafi, Shamir Rizvi and Aida Haghighi
Algorithms 2024, 17(6), 234; https://doi.org/10.3390/a17060234 - 28 May 2024
Viewed by 1230
Abstract
Price prediction tools play a significant role in small investors’ behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets [...] Read more.
Price prediction tools play a significant role in small investors’ behavior. As such, this study aims to propose a method to more effectively predict stock prices in North America. Chiefly, the study addresses crucial questions related to the relevance of news and tweets in stock-price prediction and highlights the potential value of considering such parameters in algorithmic trading strategies—particularly during times of market panic. To this end, we develop innovative multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to investigate the influence of Twitter count (TC), and news count (NC) variables on stock-price prediction under both normal and market-panic conditions. To capture the impact of these variables, we integrate technical variables with TC and NC and evaluate the prediction accuracy across different model types. We use Bloomberg Twitter count and news publication count variables in North American stock-price prediction and integrate them into MLP and LSTM neural networks to evaluate their impact during the market pandemic. The results showcase improved prediction accuracy, promising significant benefits for traders and investors. This strategic integration reflects a nuanced understanding of the market sentiment derived from public opinion on platforms like Twitter. Full article
(This article belongs to the Special Issue Recent Advances in Algorithms for Swarm Systems)
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20 pages, 891 KiB  
Article
A Quantum-Inspired Predator–Prey Algorithm for Real-Parameter Optimization
by Azal Ahmad Khan, Salman Hussain and Rohitash Chandra
Algorithms 2024, 17(1), 33; https://doi.org/10.3390/a17010033 - 12 Jan 2024
Cited by 1 | Viewed by 1981
Abstract
Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–prey algorithm [...] Read more.
Quantum computing has opened up various opportunities for the enhancement of computational power in the coming decades. We can design algorithms inspired by the principles of quantum computing, without implementing in quantum computing infrastructure. In this paper, we present the quantum predator–prey algorithm (QPPA), which fuses the fundamentals of quantum computing and swarm optimization based on a predator–prey algorithm. Our results demonstrate the efficacy of QPPA in solving complex real-parameter optimization problems with better accuracy when compared to related algorithms in the literature. QPPA achieves highly rapid convergence for relatively low- and high-dimensional optimization problems and outperforms selected traditional and advanced algorithms. This motivates the application of QPPA to real-world application problems. Full article
(This article belongs to the Special Issue Recent Advances in Algorithms for Swarm Systems)
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