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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 3912

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 Sciences, University of Copenhagen, DK-1350 Copenhagen, Denmark
Interests: multi-robots; machine learning; dynamic systems; artificial neural networks
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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Published Papers (6 papers)

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Research

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26 pages, 5646 KB  
Article
A Symmetry-Aware BAS for Improved Fuzzy Intra-Class Distance-Based Image Segmentation
by Yazhi Wang, Lei Ding and Qing Zhang
Symmetry 2025, 17(10), 1752; https://doi.org/10.3390/sym17101752 - 17 Oct 2025
Viewed by 205
Abstract
At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even [...] Read more.
At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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16 pages, 1879 KB  
Article
Parameter-Gain Accelerated ZNN Model for Solving Time-Variant Nonlinear Inequality-Equation Systems and Application on Tracking Symmetrical Trajectory
by Yihui Lei, Longyi Xu and Jialiang Chen
Symmetry 2025, 17(8), 1342; https://doi.org/10.3390/sym17081342 - 17 Aug 2025
Viewed by 457
Abstract
Time-variant nonlinear problems have always been a kind of complex research object in the field of control. The accuracy and efficiency of settling time-variant nonlinear inequality-equation (NIE) systems are often affected by the nonlinearity degree of the systems, and there are currently no [...] Read more.
Time-variant nonlinear problems have always been a kind of complex research object in the field of control. The accuracy and efficiency of settling time-variant nonlinear inequality-equation (NIE) systems are often affected by the nonlinearity degree of the systems, and there are currently no complete algorithms to settle the time-variant NIE systems effectively. To settle this class of complex systems effectively, time-variant NIE systems are first equivalently transformed into a time-variant equation by introducing a nonnegative variable. Then, through the idea of zeroing neural network (ZNN) and the role of time-variant parameter-gain functions, a parameter-gain accelerated ZNN (PGAZNN) model is proposed to solve time-variant NIE systems. Theoretically, the stability of the proposed PGAZNN model is proved by strict mathematical analysis. In addition, the PGAZNN model can achieve fixed-time convergence, and the upper-bound of convergence time is estimated. Finally, numerical simulation example and symmetry trajectory tracking are given to verify the validity and correctness of the proposed PGAZNN model. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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17 pages, 661 KB  
Article
Adaptive Learning Control for Vehicle Systems with an Asymmetric Control Gain Matrix and Non-Uniform Trial Lengths
by Yangbo Tang, Zetao Chen and Hongjun Wu
Symmetry 2025, 17(8), 1203; https://doi.org/10.3390/sym17081203 - 29 Jul 2025
Viewed by 318
Abstract
Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces [...] Read more.
Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system’s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Intelligent Control and Computing)
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22 pages, 4478 KB  
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
Cited by 1 | Viewed by 551
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 KB  
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
Cited by 1 | Viewed by 773
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 KB  
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 941
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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