Intelligent Control and Optimization in Transportation and Grid Systems: Symmetry-Guided Approaches

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 841

Special Issue Editors


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Guest Editor
College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
Interests: electrical vehicle; smart grid; optimization of transportation and energy systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: modelling and flexible control of integrated energy system; design and optimal operation of offshore multi-energy systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Energy and Electrical Engineering, Chang'an University, Xi'an 710000, China
Interests: power quality control; transportation and energy convergence

Special Issue Information

Dear Colleagues,

In previous decades, we have witnessed unprecedented technological advancement across multiple disciplines, particularly in artificial intelligence, computing power, and sensing technologies. These developments have created new opportunities to address some longstanding challenges related to infrastructure systems.

This Special Issue aims to explore the latest advancements in intelligent technologies applied to critical infrastructure systems, with attention to symmetry-related principles where applicable. As the world faces increasing challenges related to urbanization, energy efficiency, and sustainability, the integration of artificial intelligence, machine learning, and pattern recognition techniques has become essential for developing next-generation infrastructure solutions. Many neural network architectures inherently leverage symmetry properties through weight sharing, convolutional layers, and equivariant designs. Similarly, machine learning algorithms often exploit symmetry in data for dimensionality reduction, feature extraction, and pattern recognition. Emerging areas such as intelligent control systems, optimization algorithms, smart grids, and intelligent transportation systems are revolutionizing how we design, operate, and maintain our infrastructure. This Special Issue will provide a platform for researchers and practitioners to share innovative approaches, methodologies, and applications that address these challenges while promoting sustainable development.

This Special Issue welcomes original research papers, comprehensive reviews, and case studies in (but not limited to) the following areas:

  • Advanced intelligent control systems for complex engineering problems, including symmetry-preserving control strategies;
  • Novel optimization algorithms and their applications in infrastructure systems;
  • Pattern recognition and computer vision for infrastructure monitoring and maintenance;
  • Smart grid technologies with symmetrical load balancing, renewable energy integration, and energy management systems;
  • Intelligent transportation systems for urban mobility and traffic optimization;
  • Multi-agent systems and distributed intelligence in infrastructure networks;
  • Data-driven modeling for infrastructure systems;
  • Human–machine interactions in intelligent infrastructure management;
  • Sustainable and resilient infrastructure designs using symmetry-guided intelligent technologies.

Dr. Yanxia Wang
Dr. Xiandong Xu
Dr. Yong Lu
Guest Editors

Manuscript Submission Information

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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. Symmetry is an international peer-reviewed open access monthly 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

  • intelligent control systems
  • optimization algorithms
  • pattern recognition
  • smart grids
  • intelligent transportation systems
  • energy management
  • computer vision
  • renewable energy integration
  • urban mobility
  • infrastructure monitoring

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Published Papers (1 paper)

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Research

43 pages, 32899 KB  
Article
MEPEOA: A Multi-Strategy Enhanced Preschool Education Optimization Algorithm for Real-World Problems
by Shuping Ni, Chaofang Zhong, Yi Zhu and Meng Wang
Symmetry 2026, 18(1), 154; https://doi.org/10.3390/sym18010154 - 14 Jan 2026
Viewed by 344
Abstract
To address the limitations of the original Preschool Education Optimization Algorithm (PEOA) in population diversity preservation and late-stage convergence accuracy, this paper proposes a Multi-strategy Enhanced Preschool Education Optimization Algorithm (MEPEOA). The proposed algorithm integrates an improved population initialization strategy, a multi-strategy collaborative [...] Read more.
To address the limitations of the original Preschool Education Optimization Algorithm (PEOA) in population diversity preservation and late-stage convergence accuracy, this paper proposes a Multi-strategy Enhanced Preschool Education Optimization Algorithm (MEPEOA). The proposed algorithm integrates an improved population initialization strategy, a multi-strategy collaborative search mechanism, adaptive regulation, and boundary control to achieve a more effective balance between global exploration and local exploitation. The performance of MEPEOA is comprehensively evaluated on IEEE CEC2017 and CEC2022 benchmark suites and compared with several state-of-the-art metaheuristic algorithms, including EWOA, MPSO, L_SHADE, BKA, ALA, BPBO, and the original PEOA. Experimental results demonstrate that MEPEOA achieves superior optimization accuracy and stability on the majority of benchmark functions. For example, on CEC2017 with 30 dimensions, MEPEOA reduces the average fitness value of multimodal function F9 by approximately 73.6% compared with PEOA and by more than 47% compared with EWOA. In terms of stability, the standard deviation of MEPEOA on function F6 is only 4.13 × 10−3, which is several orders of magnitude lower than those of EWOA, MPSO, and BKA, indicating highly consistent convergence behavior. Furthermore, MEPEOA exhibits clear advantages in convergence speed and robustness, achieving the best Friedman mean rank across all tested benchmark suites. In addition, MEPEOA is applied to a two-dimensional grid-based path planning problem, where it consistently generates shorter and more stable collision-free paths than competing algorithms. Overall, the proposed MEPEOA demonstrates strong robustness, fast convergence, and superior stability, making it an effective and extensible solution for complex numerical optimization and practical engineering problems. Full article
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