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Mathematics
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Artificial Intelligence and Optimization in Engineering Applications
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Artificial Intelligence and Optimization in Engineering Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 1930

Special Issue Editors

Dr. Lei He

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Interests: time/sequence-dependent scheduling; meta-heuristics; evolutionary computation
Dr. Xiaolu Liu

E-Mail Website
Guest Editor
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Interests: design of experiments; optimization modeling; artificial intelligence and metaheuristics; satellite scheduling
Prof. Dr. Lining Xing

E-Mail Website
Guest Editor
1. School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
2. Key Laboratory of Collaborative Intelligence Systems, Xidian University, Xi'an 710071, China
Interests: intelligent optimization; resource scheduling; task planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guohua Wu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: planning and scheduling; swarm intelligence; evolutionary computation; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 Artificial intelligence (AI) is revolutionizing the engineering landscape, transforming traditional processes and operations into intelligent, automated systems. AI's ability to process vast amounts of data, learn from patterns, and make informed decisions in real-time is enhancing efficiency, productivity, and innovation in various engineering sectors. To further explore and showcase these advancements, this Special Issue aims to gather cutting-edge research papers that delve into the integration of AI and optimization techniques in diverse engineering settings, including but not limited to the following:

  • Manufacturing: Utilizing AI and optimization algorithms for smart factories, production planning, and supply chain management.
  • Energy: Applying AI-driven optimization techniques for energy production, grid optimization, and demand-side management.
  • Transportation: Developing AI-powered optimization models for traffic management, routing, and scheduling in logistics and transportation networks.
  • Space Research: Exploring AI techniques for data analysis, spacecraft control, satellite scheduling and autonomous mission planning.
  • Supply Chain Management: Employing AI and optimization to improve the efficiency and resilience of supply chain networks.
  • Resource Allocation: Utilizing AI to optimize the allocation of limited resources in industries such as healthcare, mining, and agriculture.

We invite researchers, academics, and industry experts to submit original, unpublished research papers that present novel AI and optimization techniques, such as meta/hyper-heuristics and machine learning methods, in these engineering applications. Contributions should demonstrate the significance of AI and optimization in enhancing engineering processes, reducing costs, and improving overall performance.

Dr. Lei He
Dr. Xiaolu Liu
Prof. Dr. Lining Xing
Prof. Dr. Guohua Wu
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. Mathematics 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 2600 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

  • artificial intelligence
  • engineering applications
  • optimization techniques
  • manufacturing
  • energy management
  • transportation
  • supply chain management
  • space research
  • real-time decision making
  • data processing
  • automated systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

28 pages, 6578 KiB  
Article
Optimal Tuning of Robot–Environment Interaction Controllers via Differential Evolution: A Case Study on (3,0) Mobile Robots
by Jesús Aldo Paredes-Ballesteros, Miguel Gabriel Villarreal-Cervantes, Saul Enrique Benitez-Garcia, Alejandro Rodríguez-Molina, Alam Gabriel Rojas-López and Victor Manuel Silva-García
Mathematics 2025, 13(11), 1789; https://doi.org/10.3390/math13111789 - 27 May 2025
Viewed by 330
Abstract
Robotic systems operating in complex environments require optimized tuned interaction controllers to ensure accurate task execution while maintaining smooth and safe behavior. This paper presents a scalarized multi-objective tuning approach based on Differential Evolution (DE) to optimize robot–environment interaction control. The method balances [...] Read more.
Robotic systems operating in complex environments require optimized tuned interaction controllers to ensure accurate task execution while maintaining smooth and safe behavior. This paper presents a scalarized multi-objective tuning approach based on Differential Evolution (DE) to optimize robot–environment interaction control. The method balances trajectory tracking accuracy and control smoothness using repulsive forces derived from potential fields modeled as virtual springs. The approach is validated on a (3,0) omnidirectional mobile robot navigating predefined trajectories with obstacles. A comparative study of five DE variants shows that DE/best/1/bin and DE/best/1/exp offer the best performance. Simulation and experimental results, including validation with an actual force sensor, confirm the method’s effectiveness and applicability in scenarios with limited sensing capabilities or model uncertainty. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Engineering Applications)
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32 pages, 7125 KiB  
Article
Predicting CO2 Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm
by Amel Ali Alhussan, Marwa Metwally and S. K. Towfek
Mathematics 2025, 13(9), 1481; https://doi.org/10.3390/math13091481 - 30 Apr 2025
Viewed by 460
Abstract
Global carbon dioxide (CO2) emissions are increasing and present substantial environmental sustainability challenges, requiring the development of accurate predictive models. Due to the non-linear and temporal nature of emissions data, traditional machine learning methods—which work well when data are structured—struggle to [...] Read more.
Global carbon dioxide (CO2) emissions are increasing and present substantial environmental sustainability challenges, requiring the development of accurate predictive models. Due to the non-linear and temporal nature of emissions data, traditional machine learning methods—which work well when data are structured—struggle to provide effective predictions. In this paper, we propose a general framework that combines advanced deep learning models (such as GRU, Bidirectional GRU (BIGRU), Stacked GRU, and Attention-based BIGRU) with a novel hybridized optimization algorithm, GGBERO, which is a combination of Greylag Goose Optimization (GGO) and Al-Biruni Earth Radius (BER). First, experiments showed that ensemble machine learning models such as CatBoost and Gradient Boosting addressed static features effectively, while time-dependent patterns proved more challenging to predict. Transitioning to recurrent neural network architectures, mainly BIGRU, enabled the modeling of sequential dependence on emissions data. The empirical results show that the GGBERO-optimized BIGRU model produced a Mean Squared Error (MSE) of 1.0 × 10−5, the best tested approach. Statistical methods like the Wilcoxon Signed Rank Test and ANOVA were employed to validate the framework’s effectiveness in improving the evaluation, confirming the significance and robustness of the improvements due to the framework. In addition to improving the accuracy of CO2 emissions forecasting, this integrated approach delivers interpretable explanations of the significant factors of CO2 emissions, aiding policymakers and researchers focused on climate change mitigation in data-driven decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Engineering Applications)
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18 pages, 2379 KiB  
Article
Using Machine Learning for the Precise Experimental Modeling of Catastrophe Phenomena: Taking the Establishment of an Experimental Mathematical Model of a Cusp-Type Catastrophe for the Zeeman Catastrophe Machine as an Example
by Shaonan Zhang and Liangshan Xiong
Mathematics 2025, 13(4), 603; https://doi.org/10.3390/math13040603 - 12 Feb 2025
Viewed by 474
Abstract
When catastrophe theory is applied to the experimental modeling of catastrophe phenomena, it is impossible to know in advance the corresponding relationship and mapping form between the parameters of the actual catastrophe mathematical model and the parameters of the canonical catastrophe mathematical model. [...] Read more.
When catastrophe theory is applied to the experimental modeling of catastrophe phenomena, it is impossible to know in advance the corresponding relationship and mapping form between the parameters of the actual catastrophe mathematical model and the parameters of the canonical catastrophe mathematical model. This gives rise to the problem in which the process of experimental modeling cannot be completed in many instances. To solve this problem, an experimental modeling method of catastrophe theory is proposed. It establishes the quantitative relationship between the actual catastrophe mathematical model and the canonical catastrophe mathematical model by assuming that the actual potential function is equal to the canonical potential function, and it uses a machine learning model to represent the diffeomorphism that can realize the error-free transformation of the two models. The method is applied to establish the experimental mathematical model of a cusp-type catastrophe for the Zeeman catastrophe machine. Through programming calculation, it is found that the prediction errors of the potential function, manifold, and bifurcation set of the established model are 0.0455%, 0.0465%, and 0.1252%, respectively. This indicates that the established model can quantitatively predict the catastrophe phenomenon. Full article
(This article belongs to the Special Issue Artificial Intelligence and Optimization in Engineering Applications)
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