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Mathematics
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Intelligent Algorithms Hybridizing Optimization, Machine Learning, and Simulation: Methods and...
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Intelligent Algorithms Hybridizing Optimization, Machine Learning, and Simulation: Methods 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: 1 January 2027 | Viewed by 4234

Editors

Prof. Dr. Angel A. Juan

E-Mail Website
Guest Editor
Department of Applied Statistics and Operations Research, Universitat Politècnica de València, 03801 Alcoy, Spain
Interests: metaheuristics; simulation; machine learning; transportation; risk analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Calvet

E-Mail Website
Guest Editor
Department of Telecommunication and Systems Engineering, Universitat Autònoma de Barcelona, 08202 Sabadell, Spain
Interests: statistics; machine learning; transportation; management
Special Issues, Collections and Topics in MDPI journals
Dr. Majsa Ammuriova

E-Mail Website
Guest Editor
1. Computer Science, Multimedia and Telecommunication Studies, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
2. Industrial Engineering Department, German Jordanian University, 11180 Amman, Jordan
Interests: optimization; simulation; heuristics; transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Mathematics journal will host a Special Issue titled “Intelligent Algorithms Hybridizing Optimization, Machine Learning, and Simulation: Methods and Applications”. This Special Issue will focus on methods that integrate optimization (exact or heuristic) with machine learning models or simulation techniques to address complex decision problems.

Recent developments in data availability and computing have supported the design of hybrid algorithms that combine traditional heuristics or exact methods with learning-based models and simulation tools. These approaches are increasingly used to solve practical problems where uncertainty, non-linearity, dynamic conditions, and large solution spaces make classical methods insufficient.

We invite contributions that present new methods, comparative analyses, or real-world applications that demonstrate the advantages of combining optimization, machine learning, and simulation. Submissions should include a clear mathematical or computational foundation. Topics may include, but are not limited to, algorithm design, performance evaluation, data-driven decision-making, and implementation of optimization-based decision-support systems.

This Special Issue is organized in collaboration with the EURO Working Group on Business Analytics and Artificial Intelligence Interfaces (https://bai.euro-online.org/), supporting its goal of advancing cross-disciplinary research in optimization and intelligent systems.

Prof. Dr. Angel A. Juan
Dr. Laura Calvet
Dr. Majsa Ammuriova
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

  • optimization
  • artificial intelligence
  • simulation
  • statistics and machine learning
  • hybrid intelligent algorithms
  • heuristics and metaheuristics
  • simheuristics and learnheuristics

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Published Papers (2 papers)

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Research

21 pages, 1425 KB  
Article
Sim-Exact Methods for Stochastic Optimization: A Complementary Approach to Simheuristics
by Angel A. Juan, Antonio R. Uguina, Marc Escoto and Veronica Medina
Mathematics 2026, 14(9), 1518; https://doi.org/10.3390/math14091518 - 30 Apr 2026
Viewed by 593
Abstract
This paper introduces a sim-exact methodology for stochastic combinatorial optimization problems. The approach combines exact optimization models with Monte Carlo or discrete-event simulation to evaluate candidate solutions under uncertainty. The method iteratively adjusts a control parameter based on simulation feedback and solves a [...] Read more.
This paper introduces a sim-exact methodology for stochastic combinatorial optimization problems. The approach combines exact optimization models with Monte Carlo or discrete-event simulation to evaluate candidate solutions under uncertainty. The method iteratively adjusts a control parameter based on simulation feedback and solves a sequence of deterministic optimization problems. Unlike scenario-based stochastic programming, the approach does not rely on explicit scenario enumeration, and unlike simheuristics, it preserves optimality with respect to each deterministic subproblem. The methodology is tested on the vehicle routing problem with stochastic demands under different levels of demand variability. Results are compared with a simheuristic approach and a sample average approximation (SAA) method. The results show that sim-exact performance is comparable to simheuristics, with no statistically significant differences in most cases, while SAA shows weaker performance under medium and high variability. Full article
(This article belongs to the Special Issue Intelligent Algorithms Hybridizing Optimization, Machine Learning, and Simulation: Methods and Applications)
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28 pages, 9784 KB  
Article
Bayesian-Optimized Ensemble Learning for Music Popularity Prediction with Shapley-Based Interpretability
by Liang Qiu, Penghui Wang, Jing Zhao, Hong Zhang and Mujiangshan Wang
Mathematics 2026, 14(6), 946; https://doi.org/10.3390/math14060946 - 11 Mar 2026
Cited by 1 | Viewed by 2940
Abstract
Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models [...] Read more.
Music popularity prediction is a fundamental problem in music information retrieval, with important implications for digital content dissemination and creative decision-making on streaming platforms. In this study, music popularity prediction is formulated as a supervised regression problem, and six widely-used tree ensemble models (Random Forest, XGBoost, CatBoost, LightGBM, Extra Trees, and Decision Tree) are systematically evaluated using large-scale Spotify data. Among these models, Random Forest achieves the best predictive performance on this dataset (RMSE = 6.79, MAE = 5.10, and R2 = 0.6658), followed by Extra Trees (R2 = 0.6378) and Decision Tree (R2 = 0.6328). Bayesian hyperparameter optimization based on a Tree-structured Parzen Estimator with an Expected Improvement acquisition function is conducted over 50 trials with 5-fold cross-validation to ensure robust model selection. Shapley value decomposition via SHAP analysis reveals that temporal recency dominates feature importance, far surpassing traditional musical attributes, while acoustic intensity (loudness) exhibits a U-shaped contribution pattern with optimal values at moderate intensity levels. Further SHAP dependence analysis uncovers non-linear relationships, indicating substantial popularity advantages for recent releases and optimal loudness levels around −5 to 0 dB. These findings suggest that streaming popularity is primarily governed by temporal exposure dynamics and production-related characteristics rather than intrinsic musical structure, offering both theoretical insights for music information retrieval research and suggestive empirical patterns that may inform future investigations into digital music ecosystems. Full article
(This article belongs to the Special Issue Intelligent Algorithms Hybridizing Optimization, Machine Learning, and Simulation: Methods and Applications)
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