Applied Optimization in Clean and Renewable Energy: New Trends

Dear Colleagues,

In recent years, the environmental pollution become a crisis and the world is facing. The direction of research is towards the utilization of renewable energy which will help in fullfilling the energy demand and also to mitigate the environmental problems. E.g. biomass.  Evolutionary algorithms are a collection of the start-of-the-art theoretical research, design challenges, and applications in the field of computer science. Multi-objective optimization is concerned with mathematical optimization problems involving more than one objective function to optimized simultanesouly. It had been applied in many fields of science, including engineering, economics, and logistics where the optimal decisions need to be made trade-offs between the conflicting objectives. Nowadays, a large amount of unstructured heterogenous data powered the demand to extract useful insights in an automatic, reliable and scalable way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many data analytical tasks, such as image processing, face recognition, video surveillance, document summarization, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and multimedia data analysis tasks. On the other hand, data science, data analytics are not simply the consumers of optimization technology but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions.

This special issue aims to seek the high-quality papers from academics and industry-related researchers in the areas of applied mathematics, renewable energy systems, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to show the most recently advanced methods, e.g. deep neural networks and learning systems, in optimization and machine learning for parallel data computations.

Prof. Ugo Fiore
Prof. Dr. Elias Munapo
Dr. Pandian Vasant
Dr. J. Joshua Thomas
Dr. Vladimir Panchenko
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 250 words) can be sent to the Editorial Office for assessment.

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.