Hybrid Machine Learning and Deep Learning Techniques for Optimization and Numerical Modeling
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 165
Special Issue Editor
Interests: bio-inspired algorithms; optimization algorithms; machine/deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hybrid machine learning and deep learning techniques combine data-driven models with metaheuristics such as PSO, ACO, and GA to solve complex optimization and numerical modeling problems. These hybrid approaches enable solvers to adapt their behavior in real time, refining parameter settings, search strategies, and pattern recognition. Recent work shows that embedding learning mechanisms improves flexibility and performance, especially in high-dimensional and combinatorial problems.
Although many studies have reported successful results, challenges remain. Most existing methods are tied to specific problem domains and struggle to transfer learned behaviors to new tasks. Additionally, the internal processes of hybrid solvers often lack transparency, limiting their adoption in fields where explainability and traceability are essential.
This Special Issue invites contributions on hybrid ML and DL methods applied to optimization and numerical modeling. Topics may include, but are not limited to, combinatorial optimization, reinforcement learning in metaheuristics, deep learning-enhanced simulations, explainable AI within optimization frameworks, transfer learning for optimization, meta-feature extraction for adaptive solvers, reactive and autonomous optimization components, search space reduction techniques, methods for tracing and interpreting solver behavior over time, cross-domain generalization, multi-level hybrid architectures, and the development of explainability metrics, visualization tools, and interpretability standards for hybrid solvers. Both methodological developments and applied studies are welcome. Review articles that provide an overview of recent advances in the field are also encouraged.
Dr. Rodrigo Olivares
Guest Editor
Manuscript Submission Information
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Keywords
- hybrid optimization algorithms
- machine learning in optimization
- deep learning for numerical modeling
- data-driven metaheuristics
- transfer learning in optimization
- explainable AI (XAI)
- reactive and adaptive solvers
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