Novel Research and Applications on Optimization Algorithms
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 August 2024 | Viewed by 203
Special Issue Editors
Interests: swarm optimization; artificial intelligence; bioinspired optimization algorithms; power systems; renewable energy sources; electric load forecasting; wind speed prediction; high voltage
Interests: power system protection; electrical power engineering; power systems simulation; power systems analysis; simulation; electrical engineering; engineering, applied and computational mathematics; transformers; power engineering; power transmission; electrostatic discharge; electromagnetic compatibility; high voltages
Special Issues, Collections and Topics in MDPI journals
Interests: transmission and distribution grids; electric vehicles; distributed generation; energy storage, energy markets; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue aims to present to the scientific community novel research and developments in the field of optimization, and we believe that your expertise would greatly contribute to our understanding of this important area.
Optimization plays a key role in tackling complex real-world problems across various areas, such as logistics, transportation, finance, healthcare, engineering, navigation and communication, environmental sciences, and many more. When researchers develop or improve the existing optimization techniques, they contribute to both the efficiency and effectiveness of solutions to the above-mentioned problems in terms of resource consumption, cost saving, and decision-making. Furthermore, by making use of recent technological advances, new approaches in optimization may lead to reduced training times, more accurate predictions, and more efficient use of computational resources, which are essential for the development of cutting-edge applications in many fields. Additionally, as challenges in natural sciences over the last years have become more complicated, optimization algorithms contribute to a more sustainable future by planning renewable energy production and thus reducing greenhouse gas emissions.
We invite cutting-edge research and both theoretical and experimental studies exploring recent advances in this field.
Prof. Dr. Stylianos Pappas
Dr. Georgios Fotis
Dr. Vasiliki Vita
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. 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.
Keywords
- advances in optimization
- artificial intelligence
- machine learning
- deep learning
- neural networks
- swarm optimization
- genetic algorithms
- processing engineering application data
