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: 28 February 2025 | Viewed by 2745
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
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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
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