Optimization and Engineering

Optimization and Engineering's Howard Rosenbrock Prize is awarded annually to honor the authors of the best paper published in the journal in the previous year.

Call for papers for the Special Issue of the Optimization and Engineering Journal on Sustainable Development of Energy, Water and Environment Systems – SDEWES, dedicated to the SDEWES 2024 Conferences

The background of this Special Issue (SI) of the Optimization and Engineering (OPTE) journal are the 2024 Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conferences. This broad field was discussed by the participants of three conferences held in 2024 – the 4th Latin American SDEWES Conference (Vina del Mar), the 2nd Asia Pacific SDEWES Conference (Gold Coast) and the 19th SDEWES Conference (Rome).

This SI aims at bringing together articles that discuss recent advances of optimization methods and algorithms that integrate various life supporting systems. Very often, a decision making problem appears in the form of a parameter estimation problem, it can be formulated as an optimization model, and then solved using different optimization algorithms and techniques. All papers included in this SI of the OPTE journal will consider aspects of numerical analysis, mathematical modeling, and computational methods involved in investigating, planning and implementing sustainable development. In this context, the guest editors have confidence that the selected papers and addressed issues will substantially contribute to the increase of the knowledge body published in the OPTE journal, and the SI will be a significant platform for researchers to discuss, share, and disseminate new ideas.

Information on the upcoming SDEWES events and other related activities can be found on the website of the International Centre for Sustainable Development of Energy, Water and Environment Systems (SDEWES Centre) at <www.sdewes.org>.

Proposed date of submission of final paper:

February 28, 2025 – first version of all papers

July 31, 2025 – final version of all papers

Guest editorial team of this special volume:

Dr. Marian Trafczynski, Managing Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: [email protected]

Prof. Krzysztof Urbaniec, Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: [email protected]

Dr. Slawomir Alabrudzinski, Guest Editor

The Faculty of Civil Engineering, Mechanics and Petrochemistry

Warsaw University of Technology, Plock, Poland

e-mail: [email protected]

Dr. Hrvoje Mikulčić, Guest Editor

University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture,

Zagreb, Croatia

e-mail: [email protected]

Submissions:

The Special Issue will be limited to the conference participants. A number of papers with archival quality, presented at the SDEWES2024 conferences, will be after the conference selected for recommendation for submission in a Special Issue of the Optimization and Engineering journal. The submission procedure requires the submission of a manuscript to the Optimization and Engineering (OPTE) journal at https://www.springer.com/journal/11081 and select the special issue “ Sustainable Development of Energy, Water and Environment Systems – SDEWES 2024”. All submissions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/journal/11081/submission-guidelines. All submitted papers will be reviewed on a peer-review basis as soon as they are received.

Editors: Andre Milzarek, Zaiwen Wen, Junfeng Yang

This special issue aims to bring together articles discussing recent advances in machine learning and optimization. Machine learning, a branch of artificial intelligence, focuses on developing algorithms that allow computers to learn from data and make decisions. Optimization involves finding the best solution from a set of feasible options and is crucial for enhancing machine learning algorithms.

The integration of these two fields offers significant potential for a wide range of applications. Machine learning techniques can improve optimization processes by predicting optimal solutions and enhancing search efficiency. Conversely, optimization methods can refine machine learning models for better accuracy and generalization, leading to breakthroughs in domains like healthcare, finance, and engineering.

We invite contributions that highlight innovative approaches and significant findings in the intersection of machine learning and optimization. Topics of interest include, but are not limited to:

- Optimization algorithms for machine learning

- Machine learning methods for optimization

- Artificial intelligence and optimization

- Applications in various fields such as healthcare, finance, and engineering

- Data-driven optimization techniques

We look forward to your submissions that push the boundaries of research in machine learning and optimization. This special issue aims to provide a platform for researchers to share their latest findings and explore new directions in this exciting and rapidly evolving field.

Important Dates: Deadline for submissions: January 31, 2025.

Submission Procedure: Please submit to the Optimization and Engineering (OPTE) journal at https://www.springer.com/mathematics/journal/11081 and select special issue “SI: Machine Learning and Optimization”. All submissions must be original and may not be under review by another publication. Interested authors should consult the journal’s “Instructions for Authors”, at https://www.springer.com/mathematics/journal/11081. All submitted papers will be reviewed on a peer review basis as soon as they are received. Accepted papers will become immediately available at Online First until the complete Special Issue appears.

Guest Editors: Dr. Jia-Bao Liu (Anhui Jianzhu University, China), Dr. Muhammad Javaid (University of Management and Technology, Pakistan), Dr. Mohammad Reza Farahani (Iran University of Science and Technology, Iran)

Submission Deadline: February 08, 2024

This special issue aims at bringing together articles that discuss recent advances in Graph Theory-based Approaches for Optimizing Neural Network Architectures. Graph theory has emerged as a powerful tool for optimizing neural network architectures. As the field of artificial intelligence continues to advance, researchers and engineers look for innovative methods to design more efficient and effective neural networks. Exploiting graph theory principles can address challenges related to model complexity, training efficiency and generalization capabilities. In Neural networks, especially deep learning models have demonstrated remarkable success in various tasks such as image recognition, natural language processing and speech synthesis. However, the increased complexity of these models comes with a trade-off. Graph theory provides a framework for modeling neural networks as graphs provided with neurons as nodes and connections as edges. We encourage submissions from researchers in this background to demonstrate the effectiveness of graph theory-based approaches on various benchmark datasets and real-world applications.