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Symmetry
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Symmetry and Asymmetry in Optimization Algorithms and System Control
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Symmetry and Asymmetry in Optimization Algorithms and System Control

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2858

Special Issue Editors

Dr. Wenjie Wang

E-Mail Website
Guest Editor
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China
Interests: intelligent and green manufacturing; mathematical modeling and simulation; digital-twin and system control; multi-agent system
Dr. Honghao Zhang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shandong University, Jinan 250061, China
Interests: structural and multidisciplinary optimization; mathematical modeling; green manufacturing; vehicle safety design; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Yuan

E-Mail
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 212000, China
Interests: digital-twin and system control; disassembly modeling and planning; AI optimization algorithm; sparse optimization
Prof. Dr. Guangdong Tian

E-Mail Website
Guest Editor
School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Interests: adaptive robust control; logistics and transportation; AI intelligent algorithm; green manufacturing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhiwu Li

E-Mail Website
Guest Editor
Institute of Systems Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau
Interests: Petri net theory and application; supervisory control of discrete event systems; workflow analysis; system reconfiguration; game theory; production scheduling and planning; data and process mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In contemporary scientific research and technological applications, the concepts of symmetry and asymmetry have emerged as critical elements in optimization algorithms and system control, for example, data encryption, information security, and discrete production systems. Symmetry, a fundamental attribute in nature and mathematics, not only simplifies problem complexity and enhances computational efficiency but also plays a unique role in system control. For example, symmetry can significantly enhance the design of efficient optimization algorithms, such as symmetric cone optimization, sparse optimization, and optimization methods based on symmetric models. By contrast, asymmetry has shown distinct advantages in complex system control, such as asymmetric consensus problems in multi-agent systems and asymmetric rule applications in complex signal control.

This Special Issue, entitled "Symmetry and Asymmetry in Optimization Algorithms and System Control," aims to gather the latest research findings from scholars worldwide to explore the applications of symmetry and asymmetry in optimization algorithms and system control. We invite researchers to submit high-quality original research papers or review articles, focusing on (but not limited to) the following areas:

  • Symmetry-based control methods;
  • Data encryption and information security;
  • Symmetry and asymmetry optimization algorithms;
  • Discrete event modeling and simulation;
  • Metaheuristic optimization algorithm design and applications;
  • Artificial intelligence (AI) in production scheduling;
  • Applications of symmetric properties in system control;
  • Interdisciplinary application in intelligent and green manufacturing.

Dr. Wenjie Wang
Dr. Honghao Zhang
Dr. Gang Yuan
Prof. Dr. Guangdong Tian
Prof. Dr. Zhiwu Li
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 250 words) can be sent to the Editorial Office for assessment.

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

  • control methods
  • data encryption
  • information security
  • optimization algorithms
  • discrete event modeling and simulation
  • production scheduling
  • system control

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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

63 pages, 13996 KB  
Article
Teaching and Research Optimization Algorithms Based on Social Networks for Global Optimization and Real Problems
by Xinyi Huang, Guangyuan Jin and Yi Fang
Symmetry 2026, 18(3), 529; https://doi.org/10.3390/sym18030529 - 19 Mar 2026
Viewed by 205
Abstract
The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive [...] Read more.
The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Optimization Algorithms and System Control)
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28 pages, 2760 KB  
Article
Human–Robot Collaborative U-Shaped Disassembly Line Balancing Using Dynamic CRITIC–Entropy and Improved Honey Badger Optimization
by Xiangwei Gao, Wenjie Wang, Yangkun Liu, Xiwang Guo, Xuesong Zhang, Bin Hu and Zhiwu Li
Symmetry 2026, 18(1), 144; https://doi.org/10.3390/sym18010144 - 12 Jan 2026
Viewed by 316
Abstract
This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under [...] Read more.
This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under key operational criteria, including idle rate, line smoothness, and energy consumption. The DTVCE framework constructs a dynamic composite score by normalizing evaluation criteria across time slices and incorporating temporal discounting to capture the evolving importance of each factor. Meanwhile, by establishing a symmetric disassembly constraint matrix to restrict the disassembly sequence and integrating exploration and exploitation mechanisms to enhance the IHBA, the solution process is empowered to efficiently generate feasible disassembly sequences and fulfill task allocation across workstations while satisfying takt time constraints. Experimental validation demonstrates that the proposed framework significantly outperforms traditional disassembly optimization approaches in both energy efficiency and line balance performance. In a case study involving an automotive drive axle, the method achieved a near-optimal configuration using only eight workstations, leading to a marked reduction in both energy consumption and idle times. Sensitivity analysis further verifies the model’s robustness, showing stable convergence and consistent performance under varying takt times and energy parameters. Overall, this study contributes to the advancement of green remanufacturing by offering a scalable, data-driven, and adaptive solution to disassembly optimization—paving the way toward sustainable and energy-aware production environments. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Optimization Algorithms and System Control)
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30 pages, 3767 KB  
Article
Enhancing Manufacturing Efficiency Through Symmetry-Aware Adaptive Ant Colony Optimization Algorithm for Integrated Process Planning and Scheduling
by Abbas Raza, Gang Yuan, Chongxin Wang, Xiaojun Liu and Tianliang Hu
Symmetry 2025, 17(6), 824; https://doi.org/10.3390/sym17060824 - 25 May 2025
Cited by 1 | Viewed by 1429
Abstract
Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This paper presents a novel optimization framework, symmetry-aware adaptive Ant Colony [...] Read more.
Integrated process planning and scheduling (IPPS) is an intricate and vital issue in smart manufacturing, requiring the coordinated optimization of both process plans and production schedules under multiple resource and precedence constraints. This paper presents a novel optimization framework, symmetry-aware adaptive Ant Colony Optimization (SA-AACO), designed to resolve key limitations in existing metaheuristic approaches. The proposed method introduces three core innovations: (1) a symmetry-awareness mechanism to eliminate redundant solutions arising from symmetrically equivalent configurations; (2) an adaptive pheromone-updating strategy that dynamically balances exploration and exploitation; and (3) a dynamic idle time penalty system, integrated with time window-based machine selection. Benchmarked across ten IPPS scenarios, SA-AACO achieves a superior makespan in 9/10 cases (e.g., 29.1% improvement over CCGA in Problem 1) and executes 18-part processing within 30 min. While MMCO marginally outperforms SA-AACO in Problem 10 (makespan: 427 vs. 483), SA-AACO’s consistent dominance across diverse scales underscores the feasibility of its application in industry to balance quality and efficiency. By unifying symmetry handling and adaptive learning, this work advances the reconfigurability of IPPS solutions for dynamic industrial environments. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Optimization Algorithms and System Control)
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