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Symmetry
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Symmetry and Asymmetry in Intelligent Control and Computing
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Symmetry and Asymmetry in Intelligent Control and Computing

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1148

Special Issue Editors

Prof. Dr. Yinyan Zhang

E-Mail Website
Guest Editor
College of Cyber Security, Jinan University, Guangzhou 510632, China
Interests: control and safety of multi-agent systems; intelligent control; dynamic neural networks
Dr. Ameer Tamoor Khan

E-Mail Website
Guest Editor
Department of Plant and Environmental Science, University of Copenhagen, Copenhagen, Denmark
Interests: artificial neural networks; multi-robots; machine learning; dynamic systems
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammed Aquil Mirza

E-Mail Website
Guest Editor Assistant
Department of Building and Real Estate, The Hong Kong Polytechnic University (PolyU), Hong Kong
Interests: AI; machine learning; wireless sensor; complex networks; optimization and control; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent control and computing manage control and computing problems by leveraging intelligent algorithms such as neural networks, fuzzy systems, and evolutionary computation. While many studies on intelligent control and computing have been published in recent decades, efforts dedicated to solving symmetry and asymmetry issues in intelligent control and computing problems and methods have seldom been reported. In control problems, such as the kinematic or dynamic control of robot manipulators, input constraints can be symmetric or asymmetric, and asymmetric input constraints are often considered a challenging issue. In this Special Issue, we aim to discuss the latest developments in addressing symmetry and asymmetry issues in intelligent control and computing problems and novel methods or algorithms for intelligent control and computing by leveraging symmetry and asymmetry.

Prof. Dr. Yinyan Zhang
Dr. Ameer Tamoor Khan
Guest Editors

Dr. Mohammed Aquil Mirza
Guest Editor Assistant

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. 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
  • computing
  • symmetry
  • asymmetry
  • robotics

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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

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22 pages, 4478 KiB  
Article
A Discrete-Time Neurodynamics Scheme for Time-Varying Nonlinear Optimization with Equation Constraints and Application to Acoustic Source Localization
by Yinqiao Cui, Zhiyuan Song, Keer Wu, Jian Yan, Chuncheng Chen and Daoheng Zhu
Symmetry 2025, 17(6), 932; https://doi.org/10.3390/sym17060932 - 12 Jun 2025
Viewed by 322
Abstract
Nonlinear optimization with equation constraints has wide applications in intelligent control systems, acoustic signal processing, etc. Thus, effectively tackling the nonlinear optimization problems with equation constraints is of great significance for the advancement of these fields. Current discrete-time neurodynamics predominantly addresses unperturbed optimization [...] Read more.
Nonlinear optimization with equation constraints has wide applications in intelligent control systems, acoustic signal processing, etc. Thus, effectively tackling the nonlinear optimization problems with equation constraints is of great significance for the advancement of these fields. Current discrete-time neurodynamics predominantly addresses unperturbed optimization scenarios, exhibiting inherent sensitivity to external noise, which limits the practical application of these methods. To address this issue, we propose a discrete-time noise-suppressed neurodynamics (DTNSN) model in this paper. First, the model integrates the static optimization stability of the gradient-based neurodynamics (GND) model with the time-varying tracking capability of the zeroing neurodynamics (ZND) model. Then, an integral feedback term is introduced to suppress external noise disturbances, thereby enhancing the robustness of the model. Additionally, to facilitate implementation on digital hardware, we employ an explicit linear three-step discretization method to obtain the proposed DTNSN model. Finally, the convergence performance, noise suppression capability, and practicality of the model are validated through theoretical analysis, numerical simulations, and acoustic source localization experiments. The model is applicable to the fields of intelligent control systems, acoustic signal processing, and industrial automation, providing new tools for real-time optimization in noisy environments. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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19 pages, 1053 KiB  
Article
Symmetry-Aware Dynamic Scheduling Optimization in Hybrid Manufacturing Flexible Job Shops Using a Time Petri Nets Improved Genetic Algorithm
by Xuanye Lin, Zhenxiong Xu, Shujun Xie, Fan Yang, Juntao Wu and Deping Li
Symmetry 2025, 17(6), 907; https://doi.org/10.3390/sym17060907 - 8 Jun 2025
Viewed by 297
Abstract
Dynamic scheduling in hybrid flexible job shops (HFJSs) presents a critical challenge in modern manufacturing systems, particularly under dynamic and uncertain conditions. These systems often exhibit inherent structural and behavioral symmetry, such as uniform machine–job relationships and repeatable event response patterns. To leverage [...] Read more.
Dynamic scheduling in hybrid flexible job shops (HFJSs) presents a critical challenge in modern manufacturing systems, particularly under dynamic and uncertain conditions. These systems often exhibit inherent structural and behavioral symmetry, such as uniform machine–job relationships and repeatable event response patterns. To leverage this, we propose a time Petri nets (TPNs) model that integrates time and logic constraints, capturing symmetric processing and setup behaviors across machines as well as dynamic job and machine events. A transition select coding mechanism is introduced, where each transition node is assigned a normalized priority value in the range [0, 1], preserving scheduling consistency and symmetry during decision-making. Furthermore, we develop a symmetry-aware time Petri nets-based improved genetic algorithm (TPGA) to solve both static and dynamic scheduling problems in HFJSs. Experimental evaluations show that TPGA significantly outperforms classical dispatching rules such as Shortest Job First (SJF) and Highest Response Ratio Next (HRN), achieving makespan reductions of 23%, 10%, and 13% in process, discrete, and hybrid manufacturing scenarios, respectively. These results highlight the potential of exploiting symmetry in system modeling and optimization for enhanced scheduling performance. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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Review

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27 pages, 391 KiB  
Review
Survey of Neurodynamic Methods for Control and Computation in Multi-Agent Systems
by Vasilios N. Katsikis, Bolin Liao and Cheng Hua
Symmetry 2025, 17(6), 936; https://doi.org/10.3390/sym17060936 - 12 Jun 2025
Viewed by 279
Abstract
Neurodynamics is recognized as a powerful tool for addressing various problems in engineering, control, and intelligent systems. Over the past decade, neurodynamics-based methods and models have been rapidly developed, particularly in emerging areas such as neural computation and multi-agent systems. In this paper, [...] Read more.
Neurodynamics is recognized as a powerful tool for addressing various problems in engineering, control, and intelligent systems. Over the past decade, neurodynamics-based methods and models have been rapidly developed, particularly in emerging areas such as neural computation and multi-agent systems. In this paper, we provide a brief survey of neurodynamics applied to computation and multi-agent systems. Specifically, we highlight key models and approaches related to time-varying computation, as well as cooperative and competitive behaviors in multi-agent systems. Furthermore, we discuss current challenges, potential opportunities, and promising future directions in this evolving field. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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