AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition

Topic Information

Dear Colleagues,

Due to novel paradigms and approaches, including agents AI and quantum computing, as well as the increasing computational capability of current data processing systems, new opportunities emerge in the modelling, simulations, and optimization of complex systems and devices. Difficult-to-apply, highly demanding, and time-consuming methods may now be considered when developing complete and sophisticated models in many areas of science and technology. Combining AI algorithms and computational methods, including numerical and other methods, allows for conducting multi-threaded analyses to solve advanced and interdisciplinary problems.

This article collection aims to bring together research on advances in the modelling, simulation, and optimization issues of complex systems, considering the great interest received for the first edition of this Topic.

Original research, review articles, and short communications focusing on (but not limited to) artificial intelligence and other computational methods are welcome.

This topic was carried out within the framework of the MsLimitCO2 project, “Multi-scale investigation of chemical looping combustion of biomass pellets towards negative CO₂ emission”, (Agreement No. WPC3/2022/44/MSLimitCo2/2024), funded through the 3rd Polish–Chinese/Chinese–Polish Joint Research Programme, managed by the National Center for Research and Development (NCBR), Poland, and the Ministry of Science and Technology (MOST) of the People’s Republic of China. The support received is gratefully acknowledged.

Prof. Dr. Jaroslaw Krzywanski
Dr. Marcin Sosnowski
Dr. Karolina Grabowska
Dr. Dorian Skrobek
Dr. Anna Zylka
Prof. Dr. Agnieszka Kijo-Kleczkowska
Dr. Bashar Shboul
Prof. Dr. Tomasz Czakiert
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	1.8	4.1	2008	18.9 Days	CHF 1600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Entropy entropy	2.1	4.9	1999	22.3 Days	CHF 2600	Submit
Machine Learning and Knowledge Extraction make	4.0	6.3	2019	20.8 Days	CHF 1800	Submit
Materials materials	3.1	5.8	2008	13.9 Days	CHF 2600	Submit
Laboratories laboratories	-	-	2024	15.0 days *	CHF 1000	Submit

* Median value for all MDPI journals in the second half of 2024.

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All Algorithms Energies Entropy MAKE Materials Laboratories

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39 pages, 4034 KiB  

Open AccessArticle

Reference Point and Grid Method-Based Evolutionary Algorithm with Entropy for Many-Objective Optimization Problems

by Qi Leng, Bo Shan and Chong Zhou

Entropy 2025, 27(5), 524; https://doi.org/10.3390/e27050524 - 14 May 2025

Viewed by 124

Abstract

In everyday scenarios, there are many challenges involving multi-objective optimization. As the count of objective functions rises to four or beyond, the problem’s complexity intensifies considerably, often making it challenging for traditional algorithms to arrive at satisfactory solutions. The non-dominated sorting evolutionary reference [...] Read more.

In everyday scenarios, there are many challenges involving multi-objective optimization. As the count of objective functions rises to four or beyond, the problem’s complexity intensifies considerably, often making it challenging for traditional algorithms to arrive at satisfactory solutions. The non-dominated sorting evolutionary reference point-based (NSGA-III) and the grid-based evolutionary algorithms (GrEA) are two prevalent algorithms for many-objective optimization. These two algorithms preserve population diversity by employing reference point and grid mechanisms, respectively. However, they still have limitations when addressing many-objective optimization problems. Due to the uniform distribution of reference points, the reference point-based methods do not obtain good performance on problems with an irregular Pareto front, while grid-based methods do not achieve good results on problems with a regular Pareto front because of the uneven partition of grids. To address the limitations of reference point-based algorithms and grid-based approaches in tackling both regular and irregular problems, a reference point- and grid-based evolutionary algorithm with entropy is proposed for many-objective optimization, denoted as RGEA, which aims to solve both regular and irregular many-objective optimization problems. Entropy is introduced to measure the shape of the Pareto front of a many-objective optimization problem. In RGEA, a parameter $\alpha$ is introduced to determine the interval for calculating the entropy value. By comparing the current entropy value with the maximum entropy value, the reference point-based method or the grid-based method can be determined. In order to verify the performance of the proposed algorithm, a comprehensive experiment was designed on some popular test suites with 3-to-10 objectives. In addition, RGEA was compared against six algorithms without adaptive technology and six algorithms with adaptive technology. A great number of experimental results were obtained showing that RGEA can obtain good results. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition)

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22 pages, 1543 KiB  

Open AccessArticle

A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder

by Xingfa Zi, Feiyi Liu, Mingyang Liu and Yang Wang

Energies 2025, 18(10), 2434; https://doi.org/10.3390/en18102434 - 9 May 2025

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Abstract

Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based [...] Read more.

Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. TiDE compresses historical time series and covariates into latent representations via residual connections and reconstructs future values through a temporal decoder, capturing both long- and short-term dependencies. We trained the model using data from 2020 to 2022 from Australia’s Desert Knowledge Australia Solar Centre (DKASC), with 2023 data used for testing. Forecast accuracy was evaluated using the $R^2$ coefficient of determination, mean absolute error (MAE), and root mean square error (RMSE). In the 5 min ahead forecasting test, TiDE demonstrated high short-term accuracy with an $R^2$ of 0.952, MAE of 0.150, and RMSE of 0.349, though performance declines for longer horizons, such as the 1 h ahead forecast, compared to other algorithms. For one-day-ahead forecasts, it achieved an $R^2$ of 0.712, an MAE of 0.507, and an RMSE of 0.856, effectively capturing medium-term weather trends but showing limited responsiveness to sudden weather changes. Further analysis indicated improved performance in cloudy and rainy weather, and seasonal analysis reveals higher accuracy in spring and autumn, with reduced accuracy in summer and winter due to extreme conditions. Additionally, we explore the TiDE model’s sensitivity to input environmental variables, algorithmic versatility, and the implications of forecasting errors on PV grid integration. These findings highlight TiDE’s superior forecasting accuracy and robust adaptability across weather conditions, while also revealing its limitations under abrupt changes. Full article

(This article belongs to the Topic AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity, Second Edition)

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