Metaheuristics and Artificial Intelligence: Latest Advances and Prospects
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 559
Special Issue Editors
2. Systems Research Institute, Polish Academy of Sciences, 30-059 Warsaw, Poland
Interests: data science; artificial neural networks; metaheuristics; swarm intelligence; evolutionary computation; fuzzy logic; machine learning; deep learning; explainable artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: neural networks; computer vision; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Metaheuristics and artificial intelligence have become increasingly important in solving complex optimization problems in various fields. Metaheuristics are powerful search algorithms that can find near-optimal solutions to problems that are difficult or impossible to solve using traditional methods. On the other hand, artificial intelligence (AI) techniques, including machine learning, deep learning, and neural networks, have shown remarkable success in solving many real-world problems.
This Special Issue in the Mathematics journal aims to provide an overview of the latest advances and prospects in the field of metaheuristics and artificial intelligence. It features contributions from leading experts in the field who present their research on new and innovative methods and techniques that have been developed to tackle complex optimization problems.
The topics covered in this Special Issue include, but are not limited to, optimization algorithms, metaheuristics, swarm intelligence, genetic algorithms, evolutionary computation, artificial neural networks, fuzzy logic, machine learning, deep learning, and hybrid systems. In addition to the aforementioned topics, this Special Issue also focuses on the importance of explainable artificial intelligence (XAI) techniques which aim to provide interpretable and transparent models and algorithms to enhance the understanding and trustworthiness of the decision-making process in AI systems. This Special Issue provides a comprehensive overview of the latest research in the field and highlights the ability of these techniques to solve a wide range of problems in diverse areas such as engineering, finance, healthcare, and transportation.
Prof. Dr. Piotr A. Kowalski
Dr. Rafal Scherer
Guest Editors
Manuscript Submission Information
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Keywords
- metaheuristics
- artificial intelligence
- optimization
- swarm intelligence
- genetic algorithms
- evolutionary computation
- artificial neural networks
- fuzzy logic
- machine learning
- deep learning
- hybrid systems
- explainable artificial intelligence
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