Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.2
Journals
Symmetry
Special Issues
Symmetry/Asymmetry in Optimization Algorithms and Systems Control
share announcement

Symmetry/Asymmetry in Optimization Algorithms and Systems Control

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2692

Special Issue Editors

Dr. Yuhe Shi

E-Mail Website
Guest Editor
School of Management, Guizhou University, Guizhou 550025, China
Interests: logistics system modeling and optimization; unmanned systems and intelligent scheduling; multi-objective optimization; intelligent simulation and modeling
Dr. Yuanyuan Zhang

E-Mail Website
Guest Editor
School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: unmanned systems optimization; control and applications of multi-agent systems; intelligent optimization algorithms and simulation

Special Issue Information

Dear Colleagues,

Optimization algorithms and systems control are fundamental to solving complex decision-making problems in various domains, including logistics, supply chain management, unmanned systems, and intelligent scheduling. Symmetry in optimization can lead to elegant and computationally efficient solutions, enabling better problem decomposition, solution space reduction, and algorithmic robustness. However, real-world systems often exhibit asymmetries due to uncertainties, dynamic environments, and heterogeneous resource constraints, necessitating adaptive and asymmetric optimization strategies.

This Special Issue aims to explore the role of symmetry and asymmetry in optimization algorithms and systems control, emphasizing the impact on computational efficiency, decision-making accuracy, and practical applicability. We welcome contributions that investigate novel theoretical advancements, algorithmic innovations, and real-world applications of symmetric and asymmetric optimization in logistics, autonomous systems, multi-objective decision-making, and intelligent modeling. Topics that are invited for submission include (but are not limited to):

  • Symmetric and asymmetric optimization
  • Convex and non-convex optimization
  • Multi-objective optimization
  • Intelligent scheduling and routing optimization
  • Metaheuristics and hybrid algorithms
  • Data-Driven Optimization
  • Machine Learning and Data Science
  • Unmanned systems optimization
  • Logistics and supply chain control
  • Dynamic resource allocation

We look forward to receiving your contributions.

Dr. Yuhe Shi
Dr. Yuanyuan Zhang
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

  • optimization algorithm
  • systems control
  • autonomous system
  • multi-objective decision-making

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

51 pages, 49435 KB  
Article
Communication-Based Social Network Search Algorithms Are Used for Numerical Optimization and Practical Applications
by Jichao Li, Luyao Chen and Chengpeng Li
Symmetry 2026, 18(5), 712; https://doi.org/10.3390/sym18050712 - 23 Apr 2026
Viewed by 221
Abstract
To enhance the performance of the Social Network Search (SNS) algorithm in solving complex numerical optimization problems, this paper proposes a Multi-strategy Enhanced Social Network Search (MESNS) algorithm. The original SNS simulates human social behaviors through four decision-making emotions—imitation, conversation, disputation, and innovation—to [...] Read more.
To enhance the performance of the Social Network Search (SNS) algorithm in solving complex numerical optimization problems, this paper proposes a Multi-strategy Enhanced Social Network Search (MESNS) algorithm. The original SNS simulates human social behaviors through four decision-making emotions—imitation, conversation, disputation, and innovation—to perform population-based search. However, its uniform emotion selection mechanism and purely random interaction strategy may reduce convergence efficiency and weaken exploitation capability, particularly in the later stages of optimization. To overcome these limitations, MESNS incorporates three improvement strategies. First, an adaptive decision-making emotion selection mechanism is developed to dynamically adjust the probabilities of exploration and exploitation behaviors according to the iteration progress, thereby promoting a more symmetric and coordinated search transition over time. Second, an elite-guided communication strategy is introduced to enhance information propagation by integrating high-quality individuals into the interaction process, which improves convergence while maintaining population diversity. Third, a dynamic interaction radius adjustment mechanism is designed to adaptively regulate the search step size, achieving a better balance and dynamic symmetry between global exploration and local refinement. Extensive experiments are conducted on the IEEE CEC2014, CEC2017, and CEC2022 benchmark suites under multiple dimensional settings. The results demonstrate that MESNS achieves superior optimization accuracy, faster convergence speed, and improved solution stability compared with several state-of-the-art metaheuristic algorithms. Furthermore, the proposed algorithm is successfully applied to the three-dimensional wireless sensor network deployment optimization problem, where it produces a more uniformly distributed and spatially balanced sensor layout, reduces coverage holes and redundant overlaps, and thus exhibits desirable symmetry in deployment structure and sensing coverage. These findings indicate that MESNS is an effective and competitive optimization framework for complex global optimization tasks with both theoretical significance and practical value from the perspective of symmetry. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Optimization Algorithms and Systems Control)
►▼ Show Figures

Figure 1

41 pages, 52463 KB  
Article
A Public Management-Based Enterprise Development Optimization Algorithm Is Used for Numerical Optimization Problems and Real-World Applications
by Cheng Niu, Chun Zhou and Chengpeng Li
Symmetry 2026, 18(4), 675; https://doi.org/10.3390/sym18040675 - 17 Apr 2026
Viewed by 270
Abstract
With the rapid development of complex engineering systems, many real-world optimization problems are characterized by high dimensionality, strong nonlinearity, and variable coupling. To address these challenges, this paper proposes a Public Management–Augmented Multi-Strategy Adaptive Enterprise Development Optimization algorithm (PMAED), which integrates adaptive differential [...] Read more.
With the rapid development of complex engineering systems, many real-world optimization problems are characterized by high dimensionality, strong nonlinearity, and variable coupling. To address these challenges, this paper proposes a Public Management–Augmented Multi-Strategy Adaptive Enterprise Development Optimization algorithm (PMAED), which integrates adaptive differential evolution, an eigen-based rotated search strategy, and a hierarchical performance governance mechanism to enhance convergence efficiency and robustness. Experimental results on the CEC2020 and CEC2022 benchmark suites demonstrate that PMAED achieves superior performance across different problem types and dimensionalities. In the Friedman ranking test, PMAED consistently obtains the best average rank (1.90 and 1.60 on CEC2020; 2.00 and 1.92 on CEC2022 for 10D and 20D, respectively), outperforming all compared algorithms. The Wilcoxon rank-sum test further confirms that PMAED achieves statistically significant improvements on the majority of benchmark functions. In high-dimensional scenarios, PMAED shows remarkable optimization accuracy, for example, achieving a mean fitness value of 1.15 × 103 on the 20-dimensional CEC2020 F1 function, significantly outperforming classical methods. In addition, PMAED is applied to a three-dimensional UAV path planning problem. The results show that the proposed method achieves the lowest average path cost (277.62) and the smallest standard deviation among all algorithms, indicating superior stability and reliability. The planned paths are smoother, safer, and more efficient compared to those generated by other methods. Overall, the proposed PMAED provides a robust and efficient solution for complex continuous optimization problems and demonstrates strong potential for real-world engineering applications. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Optimization Algorithms and Systems Control)
►▼ Show Figures

Figure 1

40 pages, 6288 KB  
Article
A Multi-Strategy Enhanced Harris Hawks Optimization Algorithm for KASDAE in Ship Maintenance Data Quality Enhancement
by Chen Zhu, Shengxiang Sun, Li Xie and Haolin Wen
Symmetry 2026, 18(2), 302; https://doi.org/10.3390/sym18020302 - 6 Feb 2026
Viewed by 279
Abstract
To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising [...] Read more.
To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Optimization Algorithms and Systems Control)
►▼ Show Figures

Figure 1

24 pages, 5549 KB  
Article
VAM-Enhanced Deep Reinforcement Learning for Cooperative Jamming Task Allocation
by Yulian Song, Xiaoshuai Li, Yang Pan, Hongwei Liu and Junan Yang
Symmetry 2026, 18(2), 295; https://doi.org/10.3390/sym18020295 - 5 Feb 2026
Viewed by 458
Abstract
This paper addresses the cooperative jamming task allocation problem for multiple jammers against multiple communication targets in dynamic electronic warfare environments. Traditional algorithms struggle with adaptability and slow decision-making. To overcome these limitations, we propose a deep reinforcement learning (DRL) method enhanced by [...] Read more.
This paper addresses the cooperative jamming task allocation problem for multiple jammers against multiple communication targets in dynamic electronic warfare environments. Traditional algorithms struggle with adaptability and slow decision-making. To overcome these limitations, we propose a deep reinforcement learning (DRL) method enhanced by an improved Vogel’s approximation method (VAM) pre-training strategy, where VAM incorporates situational matrices for initial allocation. The proposed approach aims to maximize the total jamming situational value by intelligently assigning optimal target combinations to each multi-beam jammer. Specifically, the model evaluates the situational value of each target by integrating factors including the distance, target firepower, and threat levels, while adhering to system constraints of both jamming and target platforms. To meet the real-time decision-making requirements in dynamic adversarial environments, we integrate VAM with the proximal policy optimization (PPO) algorithm, leveraging human knowledge to accelerate the training process of DRL. Simulation results demonstrate that the proposed algorithm improves both the training efficiency and decision-making timeliness of the jamming allocation model, achieving cumulative reward increases of 38.45% and 13.86% over the respective baselines, while ensuring target coverage and effectively avoiding redundant or excessive jamming. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Optimization Algorithms and Systems Control)
►▼ Show Figures

Figure 1

20 pages, 3357 KB  
Article
Time-Varying Current Estimation Method for SINS/DVL Integrated Navigation Based on Augmented Observation Algorithm
by Xin Chen, Hongwei Bian, Fangneng Li, Rongying Wang, Yaojin Hu and Jingshu Li
Symmetry 2025, 17(11), 1881; https://doi.org/10.3390/sym17111881 - 5 Nov 2025
Cited by 2 | Viewed by 842
Abstract
To address the problem of the bottom velocity being directly affected by the time-varying ocean currents when DVL operates in the water observation mode, it cannot be directly used for combined SINS/DVL navigation. Existing methods generally approximate small-scale, short-term currents as constant; however, [...] Read more.
To address the problem of the bottom velocity being directly affected by the time-varying ocean currents when DVL operates in the water observation mode, it cannot be directly used for combined SINS/DVL navigation. Existing methods generally approximate small-scale, short-term currents as constant; however, this assumption is inconsistent with reality over longer durations. When the conventional Kalman filter (KF) algorithm incorporates currents into the state vector, their velocities become entangled with the SINS errors, limiting estimation accuracy. This paper proposes an augmented observation algorithm (AOA) that achieves error decoupling by enhancing DVL observation and deriving the observable current velocity equation without needing external observation information. This approach effectively estimates time-varying currents. The results from simulations and shipboard tests show that, compared to the reference algorithm (Augmented Observation Quantity Filtering algorithm (AOQ)), the proposed AOA significantly decreases the root mean square error (RMSE) of time-varying current velocity estimation by more than 67%. Additionally, the RMSE of the positioning accuracy of the combined SINS/DVL navigation is improved by over 68%. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Optimization Algorithms and Systems Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop