Learning-Based Multiobjective Optimization: Theory, Methods and Applications

Dear Colleagues,

The Special Issue on "Learning-Based Multiobjective Optimization: Theory, Methods, and Applications" in the journal Applied Sciences focuses on the cutting-edge developments and applications of learning-based approaches to multiobjective optimization. This research area is of paramount importance due to its wide range of applications in various scientific and engineering disciplines, including artificial intelligence, machine learning, operations research, and systems engineering.

Introduction

Scientific Background and Importance of the Research Area

Multiobjective optimization involves solving problems that require the simultaneous optimization of multiple conflicting objectives. Traditional optimization techniques often struggle with the complexity and high dimensionality of these problems. Recent advancements in machine learning and artificial intelligence have paved the way for innovative approaches to tackle these challenges more effectively.

Learning-based multiobjective optimization leverages the power of machine learning algorithms to improve the efficiency, accuracy, and scalability of optimization processes. These methods can adapt to complex problem landscapes, learn from data, and provide more robust and high-quality solutions. This research area is critically important as it addresses the growing demand for sophisticated optimization techniques that are capable of handling real-world problems across various domains, including manufacturing, logistics, healthcare, and environmental management.

Suggested Themes and Article Types

Submissions should focus on, but are not limited to, the following themes:

1. The development of novel learning-based multiobjective optimization algorithms.
2. Applications of learning-based optimization in real-world scenarios, such as manufacturing, logistics, healthcare, and environmental management.
3. Comparative studies evaluating the performance of different optimization techniques.
4. Advances in computational methods and tools that facilitate efficient multiobjective optimization.
5. The integration of machine learning techniques with multiobjective optimization frameworks.
6. Theoretical insights into the convergence, robustness, and scalability of learning-based optimization methods.
7. Case studies demonstrating the practical benefits and challenges of implementing these approaches in various industries.

Conclusion

We look forward to receiving high-quality contributions that will enhance the understanding and application of learning-based multiobjective optimization, ultimately driving innovation and progress in this dynamic field.

Dr. Reza Moghdani
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

• multiobjective optimization
• learning-based optimization
• optimization algorithms
• theoretical foundations
• real-world applications
• machine learning integration
• robustness and scalability
• industrial applications
• operations research
• logistics and supply chain
• manufacturing optimization