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Mathematics
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Evolutionary Algorithms for Engineering Optimization, Fuzzy Control, and Decision Systems
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Evolutionary Algorithms for Engineering Optimization, Fuzzy Control, and Decision Systems

A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 20 January 2027 | Viewed by 1249

Editors

Prof. Dr. José Gabriel Ríos Moreno

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: signal processing; modeling; prediction, optimization, energy sustainability and control systems for intelligent buildings
Prof. Dr. Mario Trejo-Perea

E-Mail Website
Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico
Interests: energy consumption; prediction; neural networks models; materials; optimization; fuzzy control; decision systems in intelligent buildings
Prof. Dr. Roberto Valentin Carrillo-Serrano

E-Mail Website
Guest Editor
Dirección de Investigación y Posgrado de la Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro, Mexico
Interests: robot manipulator; control of electrical machines; control of mechatronic systems; renewable energies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wanquan Liu

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: intelligent control; sparse optimisation; robot control; reinforcement learning; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing complexity of engineering systems, together with the need for accurate decision-making in uncertain environments, has driven the development of evolutionary algorithms and fuzzy techniques as key tools in modern computational intelligence. This Special Issue focuses on recent advances in evolutionary optimization, fuzzy control, and intelligent decision-making systems, encouraging the integration of bio-inspired methods, hybrid approaches, and models capable of operating under uncertainty. Authors are invited to submit theoretical, experimental, and applied contributions that expand the state of the art in these areas and demonstrate their impact on real-world engineering and applied science problems.

The purpose of this Special Issue is to gather recent contributions related to the design, analysis, and application of evolutionary algorithms and metaheuristic techniques in engineering optimization, fuzzy control, and intelligent decision-making systems. Since many theoretical and practical problems involve uncertainty, nonlinearity, and complex constraints, evolutionary and bio-inspired methods have proven to serve as fundamental tools for developing robust, efficient, and adaptive solutions. This Special Issue invites researchers and professionals in engineering, applied mathematics, artificial intelligence, automatic control, and computational sciences to submit original research articles, comparative studies, reviews, and hybrid developments that integrate evolutionary algorithms with fuzzy modeling, advanced optimization, and intelligent decision systems.

Topics of interest include, but are not limited to, the following:

  • Evolutionary algorithms applied to engineering optimization.
  • Metaheuristics for constrained optimization.
  • Bio-inspired algorithms for fuzzy control and intelligent systems.
  • Optimization of membership functions and fuzzy rule bases.
  • Hybrid methods combining fuzzy logic and evolutionary computation.
  • Multi-objective optimization in engineering and fuzzy systems.
  • Modeling and decision-making under uncertainty using evolutionary techniques.
  • Swarm intelligence applied to control and decision systems.
  • Convergence, stability, and robustness analysis of metaheuristics.
  • Applications in robotics, energy, transportation, manufacturing, automation, and healthcare.

This Special Issue aims to serve as an interdisciplinary forum connecting evolutionary optimization, fuzzy systems, and modern engineering, promoting new theoretical developments and high-impact applications.

Prof. Dr. José Gabriel Ríos Moreno
Prof. Dr. Mario Trejo-Perea
Prof. Dr. Roberto Valentin Carrillo-Serrano
Prof. Dr. Wanquan Liu
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-anonymized 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

  • evolutionary algorithms
  • engineering optimization
  • fuzzy control
  • decision systems
  • metaheuristics
  • bio-inspired optimization
  • swarm intelligence
  • multi-objective optimization
  • uncertainty modeling
  • soft computing

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Published Papers (2 papers)

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25 pages, 8119 KB  
Article
A Bee Colony Optimization Framework with Fuzzy Softmax Confidence Modeling for Multiclass Brain Tumor MRI Classification
by Nebojša Ralević, Nataša Milosavljević, Zoran Ovcin and Ljubo Nedović
Mathematics 2026, 14(13), 2444; https://doi.org/10.3390/math14132444 - 7 Jul 2026
Viewed by 346
Abstract
Brain tumor classification from magnetic resonance imaging (MRI) remains challenging in settings where only image-level labels are available and tumor classes exhibit overlapping visual characteristics. In this study, we consider the publicly available Brain Tumor MRI Dataset from Kaggle, a four-class dataset composed [...] Read more.
Brain tumor classification from magnetic resonance imaging (MRI) remains challenging in settings where only image-level labels are available and tumor classes exhibit overlapping visual characteristics. In this study, we consider the publicly available Brain Tumor MRI Dataset from Kaggle, a four-class dataset composed of 2D MRI slices belonging to the categories glioma, meningioma, pituitary tumor, and no tumor. Accordingly, the proposed framework is formulated as a slice-based multiclass classification approach rather than a volumetric 3D analysis pipeline. We propose a lightweight and interpretable framework that integrates handcrafted multiscale MRI descriptors, an artificial neural network (ANN), Bee Colony Optimization (BCO)-based neural architecture search, and fuzzy softmax confidence modeling. Each MRI slice is represented by a compact 9-dimensional feature vector derived from intensity, local entropy, and gradient magnitude computed globally and over non-overlapping spatial blocks. The ANN design problem is formulated as a discrete–continuous optimization task, where BCO is employed to optimize network architecture and training hyperparameters by maximizing validation macro-F1. To quantify predictive reliability, the softmax outputs are interpreted as fuzzy class memberships and further analyzed using maximum membership, normalized entropy, decision margin, and ambiguity measures, enabling confidence-aware reliability assessment. These fuzzy confidence descriptors enable confidence-threshold-based selective classification and rejection of low-confidence predictions. Across repeated runs, the optimized BCO-ANN achieved a mean test accuracy of 0.781±0.009, mean macro-F1 of 0.775±0.010, mean Brier score of 0.319±0.012, and mean Expected Calibration Error (ECE) of 0.0273±0.0080, compared with 0.748±0.011, 0.738±0.013, 0.352±0.010, and 0.0446±0.0071 for the baseline ANN, respectively. Under confidence-threshold-based rejection, selective macro-F1 increased to 0.820±0.009 at τ=0.55 and to 0.874±0.020 at τ=0.85, with the expected reduction in coverage. These results indicate that the proposed framework provides a transparent and reproducible approach for optimization-aware and confidence-aware multiclass brain tumor MRI classification in a lightweight handcrafted feature setting. Full article
(This article belongs to the Special Issue Evolutionary Algorithms for Engineering Optimization, Fuzzy Control, and Decision Systems)
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21 pages, 3774 KB  
Article
Discrete-Time Fourier Series Neural Network Control for Nonlinear SISO Systems: Validated in a Magnetic Levitation Model
by Sergio Miguel Delfín-Prieto, Roberto Valentín Carrillo-Serrano, Ernesto Chavero-Navarrete, José Gabriel Ríos-Moreno and Mario Trejo-Perea
Mathematics 2026, 14(10), 1649; https://doi.org/10.3390/math14101649 - 13 May 2026
Viewed by 419
Abstract
The control of nonlinear, open-loop unstable dynamics is a prevalent engineering challenge, often benchmarked through magnetic levitation (Maglev) systems. While continuous-time adaptive neural networks are commonly used to reject disturbances, their direct digital implementation often induces closed-loop instability due to unaccounted sampling effects. [...] Read more.
The control of nonlinear, open-loop unstable dynamics is a prevalent engineering challenge, often benchmarked through magnetic levitation (Maglev) systems. While continuous-time adaptive neural networks are commonly used to reject disturbances, their direct digital implementation often induces closed-loop instability due to unaccounted sampling effects. To address this, this paper proposes a discrete-time Fourier Series Neural Network (FSNN) control architecture for nonlinear Single-Input Single-Output (SISO) systems that can be transformed into the Brunovsky canonical form. The parameter adaptation laws are synthesized strictly in the discrete-time domain using Lyapunov stability theory. This approach yields an explicit upper bound for the digital sampling period, ensuring a proper implementation. Furthermore, it guarantees the Uniform Ultimate Boundedness (UUB) of the tracking error in the presence of bounded unmodeled dynamics and periodic disturbances. Numerical simulations of Maglev dynamics validate the theoretical bounds, demonstrating that the FSNN controller achieves rapid learning and generates a smooth control effort. Ultimately, by eliminating the instability risks of continuous-time approximations, this methodology bridges the gap between theoretical design and digital implementation, providing a practical framework for the robust control of electromagnetic actuators and other nonlinear industrial processes. Full article
(This article belongs to the Special Issue Evolutionary Algorithms for Engineering Optimization, Fuzzy Control, and Decision Systems)
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