Stochastic Optimization and Metaheuristic Optimization: Theory and Applications
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".
Deadline for manuscript submissions: 31 May 2024 | Viewed by 1193
Special Issue Editors
Interests: evolutionary optimisation; machine learning; STEM education
Special Issue Information
Dear Colleagues,
Stochastic and metaheuristic optimization methods are optimization algorithms incorporating probabilistic elements, either in problem data or in algorithms themselves. Due to their flexible representations and higher performances, stochastic and metaheuristic algorithms have been widely applied in solving complicated optimization problems. In machine learning, these methods can be applied in multi-layer neural network optimization, wireless sensor network optimization, data clustering, and image processing, etc.
The aim of this Special Issue is to invite researchers to report their latest and most innovative research on the development of stochastic and metaheuristic methods in machine learning applications. Contributions to this Special Issue should fall within the scope of Axioms, which comprises the following topics:
- Mathematical logic;
- Mathematical problems of artificial intelligence;
- Complex networks from mathematical viewpoints;
- Reasoning under uncertainty;
- Interdisciplinary applications of mathematical theory.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Stochastic and metaheuristic modelling;
- Mathematical modelling;
- Statistical analysis on stochastic methods;
- Stochastic methods in artificial neural network optimization;
- Stochastic and metaheuristic methods in image processing;
- Resource scheduling applications;
- Location science applications;
- Distributed computing applications;
- Stochastic and metaheuristic methods in cyber security.
We look forward to receiving your contributions.
Dr. Lily D. Li
Dr. Shang Gao
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. Axioms 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 2400 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
- stochastic optimization
- metaheuristic
- machine learning
- stochastic and metaheuristic modelling
- mathematical modelling
- statistical analysis on stochastic methods
- stochastic methods in artificial neural network optimization
- stochastic and metaheuristic methods in image processing
- resource scheduling applications
- location science applications
- distributed computing applications
- stochastic and metaheuristic methods in cyber security
