Meta-Heuristics and Machine Learning in Modelling, Developing and Optimising Complex Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 1206
Special Issue Editors
Interests: data science; machine learning; optimization algorithms; evolutionary algorithms; swarm intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: computational intelligence; evolutionary algorithms; computer vision; optical metrology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, complex systems have been applied in much of real life, such as studying self-organisation and critical phenomena from physics, spontaneous order from the social sciences, chaos from mathematics, adaptation from biology, and many others. Complex systems are, therefore, often used as a broad term encompassing a research approach to problems in many diverse disciplines, including statistical physics, information theory, nonlinear dynamics, computer science, meteorology, anthropology, sociology, economics, psychology, and biology. A metaheuristic is an advanced program that proposes a set of procedures or strategies to design heuristic optimisation algorithms. Machine learning is a branch of artificial intelligence (AI) that focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy. Advanced machine learning and optimisation algorithms have been rapidly expanding to solve various aspects of complex systems.
This Special Issue is devoted to showing the application of classic, hybrid, combinatorial, and novel metaheuristics and machine learning in the modelling, development, and optimisation of complex systems. Review and survey articles on the following topics are also encouraged for submission.
Topics of interest for publication include but are not limited to:
- Rethinking and application of the classic optimisation and machine learning algorithms for complex systems;
- Hybridisation insights into optimisation and machine learning algorithms for solving complex systems;
- Machine-learning-based predictive modelling in modelling complex systems;
- Advanced metaheuristics and machine learning algorithms in complex systems optimisation challenges;
- Adversarial machine learning (ML) applications for complex systems modelling;
- The application of modern optimisation and machine learning algorithms handling nonlinearity, spontaneous order, and adaptation in complex systems.
Dr. Mehdi Neshat
Dr. Francisco Cuevas De La Rosa
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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
- complex systems
- meta-heuristics
- machine learning
- deep learning
- transfer learning
- optimisation
- evolutionary algorithms
- swarm intelligence
- genetic algorithms
- mathematical optimization
