AI, Machine Learning and Optimization

Dear Colleagues,

Artificial intelligence (AI), machine learning (ML), and optimization techniques have revolutionized numerous fields in recent years, from healthcare and finance to manufacturing and transportation. The rapid advancement in computational capabilities has enabled increasingly complex AI and ML models, which in turn rely on sophisticated optimization methods to achieve their performance. Simultaneously, AI and ML approaches have enhanced traditional optimization techniques, leading to novel hybrid methods capable of efficiently finding high-quality solutions to computationally intractable problems and accelerating optimization processes for complex real-world applications.

This Special Issue on "AI, Machine Learning and Optimization" aims to bring together researchers and practitioners working at the intersection of these fields to share recent developments, innovative applications, and emerging trends. The interplay between AI, ML, and optimization presents rich opportunities for interdisciplinary research that can address pressing challenges across various domains. We welcome original research articles, comprehensive reviews, and case studies that showcase theoretical advances, algorithmic innovations, or novel applications in these areas.

Topics to be covered include the following:

• Deep learning architectures and optimization techniques.
• Reinforcement learning algorithms and applications.
• Metaheuristic and nature-inspired optimization methods.
• Explainable AI and interpretable machine learning models.
• Optimization methods for training neural networks.
• Multi-objective and constrained optimization.
• Evolutionary computation and genetic algorithms.
• Bayesian optimization and probabilistic methods.
• Graph neural networks and combinatorial optimization.
• Transfer learning and domain adaptation techniques.
• Time series forecasting and prediction models.
• AI and ML applications in operations research.
• Optimization under uncertainty and robust methods.
• Transformer models.
• Natural Language Processing.
• Approximation of NP-hard problems.
• Neural Architecture Search (NAS).
• Algorithm selection and configuration

Dr. John Abela
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 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. 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.

• artificial intelligence
• machine learning
• deep learning
• optimization algorithms
• meta-heuristics
• neural networks
• reinforcement learning
• evolutionary computation
• Bayesian optimization
• mathematical programming
• combinatorial optimization
• constraint satisfaction
• federated learning
• meta-heuristics
• stochastic optimization
• explainable AI
• autonomous systems
• cognitive computing
• algorithm discovery
• neural architecture search