About this Research Topic
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 past several decades, mathematical optimization has attracted much 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 non-traditional algorithms offer very flexible and successful possibilities, and high-quality solutions can often be found by applying these. Furthermore, such approaches can be enhanced for refined solutions by combining population-based optimization algorithms with improvement techniques such as local search strategies and individual learning procedures. 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 challenges, such as the algorithm design and analysis. Additionally, some researchers have raised the issue that performance comparison via a large number of experimental tests cannot reveal the true strengths and weaknesses of the optimization algorithms. In particular, a few recent studies have shown that the positive performance of some algorithms depends on the special characteristics of the test problems.
This Research Topic 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.
Topics of interest 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 of 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-objective and multi-objective optimization algorithms
Solving an optimization problem exactly may be very difficult (or even impossible) in practice, but non-traditional algorithms offer very flexible and successful possibilities, and high-quality solutions can often be found by applying these. Furthermore, such approaches can be enhanced for refined solutions by combining population-based optimization algorithms with improvement techniques such as local search strategies and individual learning procedures. 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 challenges, such as the algorithm design and analysis. Additionally, some researchers have raised the issue that performance comparison via a large number of experimental tests cannot reveal the true strengths and weaknesses of the optimization algorithms. In particular, a few recent studies have shown that the positive performance of some algorithms depends on the special characteristics of the test problems.
This Research Topic 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.
Topics of interest 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 of 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-objective and multi-objective optimization algorithms
Keywords: optimization algorithms, computational intelligence, machine learning, data analytics, swarm intelligence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
