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Mathematics
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Advances in Computational Intelligence and Applications
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Advances in Computational Intelligence and Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1740

Special Issue Editors

Dr. Chaodong Fan

E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: computational intelligence; optimization algorithm; pattern recognition; intelligent systems
Dr. Leyi Xiao

E-Mail Website
Guest Editor
School of Computer Science and Technology, Hainan University, Haikou 570228, China
Interests: computational intelligence; image processing; pattern recognition; intelligent systems
Prof. Dr. Yingjie Zhang

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Interests: artificial intelligence; intelligent control; computer control; energy-saving control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) is a rapidly evolving field that has been widely adopted across industries to tackle complex, cross-disciplinary challenges. By leveraging advanced algorithms, such as evolutionary computation, swarm intelligence, fuzzy systems, and deep learning, CI has significantly contributed to the optimization and intelligent development of models. It provides innovative solutions for problems that traditional methods struggle to address. Despite the advancements, numerous technical challenges remain, necessitating further exploration and development.

This Special Issue aims to highlight the latest research in CI and its diverse applications. It serves as a platform for both academic and industry researchers to share new theories, methodologies, and practical insights. We welcome contributions focusing on theoretical advancements, algorithm development, and real-world applications in fields such as intelligent robotics, smart healthcare, sustainable energy, and industrial automation. Furthermore, we encourage research addressing emerging challenges in big data analytics, intelligent systems, and human–machine interaction.

We invite researchers and professionals in the field to submit original research, application-oriented studies, or comprehensive reviews related to the theme of this Special Issue. Together, we aim to explore and expand the frontiers of computational intelligence, driving its continued growth and application in both academia and industry while providing strong theoretical foundations and technological solutions to foster innovation across various sectors.

Dr. Chaodong Fan
Dr. Leyi Xiao
Prof. Dr. Yingjie Zhang
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 250 words) can be sent to the Editorial Office for assessment.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • artificial intelligence
  • intelligent optimization
  • swarm intelligence
  • tensor heterogeneous computing
  • fuzzy systems
  • artificial neural networks
  • machine learning
  • deep learning
  • reinforcement learning
  • data-driven optimization
  • intelligent transportation systems
  • pattern recognition
  • autonomous driving
  • traffic volume prediction
  • traffic signal control
  • railway intelligent scheduling algorithms
  • itinerary planning
  • social network analysis
  • community detection
  • network influence maximization
  • Internet of Things
  • smart healthcare

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Published Papers (2 papers)

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42 pages, 17471 KB  
Article
MESETO: A Multi-Strategy Enhanced Stock Exchange Trading Optimization Algorithm for Global Optimization and Economic Dispatch
by Yao Zhang, Jiaxuan Lu and Xiao Yang
Mathematics 2026, 14(6), 981; https://doi.org/10.3390/math14060981 - 13 Mar 2026
Viewed by 346
Abstract
High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy [...] Read more.
High-dimensional global optimization and microgrid economic scheduling problems are often dominated by nonlinear search landscapes, strong coupling among decision variables, and stringent operational constraints, which severely limit the effectiveness of conventional metaheuristic approaches. In response to these challenges, this study presents a multi-strategy cooperative optimization framework derived from stock exchange trading principles, referred to as MESETO. The proposed method departs from the single-path evolutionary process of the standard SETO algorithm by introducing a diversified strategy collaboration mechanism that enables the dynamic adjustment of search behaviors throughout the optimization process. Multiple complementary update strategies are jointly employed to balance global exploration and local exploitation, while an adaptive probability regulation scheme continuously reallocates computational effort toward strategies that demonstrate superior performance. In addition, a solution validation mechanism is incorporated to prevent population degradation by rejecting ineffective evolutionary moves, thereby enhancing convergence stability. Extensive numerical experiments conducted on the CEC2017 and CEC2022 benchmark suites across different dimensional configurations demonstrate that MESETO consistently achieves improved solution accuracy, faster convergence, and stronger robustness compared with several representative state-of-the-art metaheuristic algorithms. Furthermore, the applicability of the proposed optimizer is verified through a 24 h microgrid economic scheduling case that integrates renewable energy sources, energy storage systems, dispatchable generators, and grid interaction. Simulation results confirm that MESETO effectively reduces operational costs while maintaining stable and efficient scheduling performance. Overall, the results indicate that MESETO constitutes a reliable and efficient optimization framework for solving complex global optimization problems and practical energy management applications. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence and Applications)
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34 pages, 2403 KB  
Article
Literary Language Mashup: Curating Fictions with Large Language Models
by Gerardo Aleman Manzanarez, Raul Monroy, Jorge Garcia Flores and Hiram Calvo
Mathematics 2026, 14(2), 210; https://doi.org/10.3390/math14020210 - 6 Jan 2026
Viewed by 719
Abstract
The artificial generation of text by computers has been a field of study in computer science since the beginning of the twentieth century, from Markov chains to Turing tests. This has evolved into automatic summarization and marketing chatbots. The generation of literary texts [...] Read more.
The artificial generation of text by computers has been a field of study in computer science since the beginning of the twentieth century, from Markov chains to Turing tests. This has evolved into automatic summarization and marketing chatbots. The generation of literary texts by Large Language Models (LLMs) has also been an area of scholarly inquiry for over six decades. The literary quality of AI-generated text can be evaluated with GrAImes, an evaluation protocol grounded in literary theory and inspired by the editorial process of book publishers. This evaluation can also be framed as part of broader editorial practices within publishing, emphasizing both theoretical grounding and applied assessment. This protocol necessitates the involvement of human judges to validate the texts generated, a process that is often resource-intensive in terms of both time and financial investment, primarily due to the specialized credentials and expertise required of these evaluators. In this paper, we propose an alternative approach by employing LLMs themselves as evaluators within the GrAImes framework. We apply this methodology to assess human-written and AI-generated microfictions in Spanish, to five PhD professors in literature and sixteen literary enthusiasts, and to short stories in both Spanish and English. By comparing the evaluations performed by LLMs with those of human judges, we examine the degree of alignment and divergence between both perspectives, thereby assessing the feasibility of LLMs as auxiliary literary evaluators. Our analysis focuses on the alignment of responses from LLMs with those of human evaluators, providing insights into the potential of LLMs in literary assessment. The conducted experiments reveal that while LLMs cannot be regarded as substitutes for human judges in the evaluation of literary microfictions and short stories, with a Krippendorff’a alpha reliability coefficient less than 0.66, they can serve as a valuable tool that offers an initial perspective on the editorial quality of the texts in question. Overall, this study contributes to the ongoing discourse on the role of artificial intelligence in literature, underlining both its methodological constraints and its potential as a complementary resource for literary evaluation. Full article
(This article belongs to the Special Issue Advances in Computational Intelligence and Applications)
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