Advances in Analysis and Application of Mathematical Optimization Algorithms
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 16947
Special Issue Editors
Interests: computational intelligence; neural networks; optimization
Special Issues, Collections and Topics in MDPI journals
Interests: Data Science and Machine Learning; Applied Mathematics for Machine Learning; Medical Image Analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In mathematics, computer science and operations research, mathematical optimization (or mathematical programming) refers to a collection of methods and techniques used for solving an optimization problem such as minimizing or maximizing an objective function. Over the last several decades, mathematical optimization has drawn a lot of attention due to its significance in many real-world applications, such as business, management, and engineering. Solving an optimization problem exactly may be very difficult or even impossible in practice, but by applying non-traditional algorithms being one very flexible and successful possibility, can often find some high-quality solutions. Besides, by combining population-based optimization algorithms with improvement techniques such as local search strategies and individual learning procedures, the capability of the algorithms could be enhanced for refined solutions. These algorithms exhibit good performance on various benchmark problems and real-world applications. As with problem-dependent improvement techniques, generating optimal solutions by the aforementioned approaches poses several unique issues such as the algorithm design and analysis. Besides, some scholars pointed out that the performance comparison via a large number of experiment tests cannot reveal the real strengths and weaknesses of the optimization algorithms. In particular, a few recent studies have shown that the good performance of some algorithms depends on the special characteristics of the test problems. This Special Issue will accept original research and review articles on novel mathematical optimization techniques and their applications. We also welcome analysis and design of optimization test problems, as well as performance evaluation indicators.
Potential topics include but are not limited to the following:
- Theoretical analyses of optimization algorithms
- Novel techniques and their applications
- Thorough analysis and comparison of existing optimization algorithms
- Analysis and design of optimization test problems and performance evaluation indicators
- Optimization methods and techniques in machine learning
- Application mathematical optimization in big data analytics
- Optimization of machine learning and deep learning models
- Robust optimization algorithms and their applications
- Optimization techniques for IoT Applications
- Single and multi- objective optimization algorithms.
Dr. Man-Fai Leung
Dr. Wenming Cao
Dr. Hangjun Che
Guest Editors
Manuscript Submission Information
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Keywords
- optimization
- neural networks
- swarm intelligence
- multi-objective Optimization
- machine learning
- applications
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