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Symmetry
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Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics
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Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 4566

Special Issue Editor

Prof. Dr. Peng-Yeng Yin

E-Mail Website
Guest Editor
Information Technology and Management Program, Ming Chuan University, Taoyuan City 333, Taiwan
Interests: artificial intelligence; evolutionary computation; wind and solar energy; metaheuristics; pattern recognition; image processing; machine learning; software engineering; computational intelligence; operations research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

“Symmetry” occurs in almost every discipline related to information technology and management. Its elements appear in many applications, including object recognition, engineering design, music composition, and market prediction. By using the properties of “symmetry”, intelligent systems such as the Internet of Things (IoT), Cyber–Physical Systems (CPSs), artificial intelligence (AI), and deep metaheuristics can be made more efficient and effective. There has been considerable innovation in industrial technology and competitiveness between businesses in the last decade. This is partly due to the emergence of new technologies such as IoT, CPS, and deep learning, as well as self-media and the circular economy, and partly due to international trade protection, which constrains the development of global supply chains and regional free trade agreements. The aim of this Special Issue is to highlight important trends in the use of symmetry/asymmetry in artificial intelligence and deep metaheuristics. We welcome original research and review articles on topics including (but not limited to) the following:

  • Symmetry/asymmetry in artificial intelligence;
  • Symmetry/asymmetry in deep metaheuristics;
  • Symmetry/asymmetry in mathematical programming;
  • Genuine symmetry/asymmetry algorithms;
  • IoT, CPS, AI, big data, and data mining to solve practical problems;
  • Artificial intelligence and deep metaheuristics in emerging industry;
  • Artificial intelligence and deep metaheuristics in the circular economy;
  • Artificial intelligence and deep metaheuristics in renewable energy;
  • Artificial intelligence and deep metaheuristics in sustainable development;
  • Artificial intelligence and deep metaheuristics in business, finance, economy, and tourism.

Prof. Dr. Peng-Yeng Yin
Guest Editor

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

  • symmetry
  • asymmetry
  • Internet of Things (IoT)
  • big data
  • artificial intelligence
  • metaheuristic

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Published Papers (4 papers)

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Research

33 pages, 8411 KB  
Article
Metaheuristic Optimization of Hybrid Renewable Energy Systems Under Asymmetric Cost-Reliability Objectives: NSGA-II and MOPSO Approaches
by Amal Hadj Slama, Lotfi Saidi, Majdi Saidi and Mohamed Benbouzid
Symmetry 2025, 17(9), 1412; https://doi.org/10.3390/sym17091412 - 31 Aug 2025
Viewed by 375
Abstract
This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as [...] Read more.
This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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18 pages, 1447 KB  
Article
Symmetry-Guided Surrogate-Assisted NSGA-II for Multi-Objective Optimization of Renewable Energy Systems
by Manuel J. C. S. Reis
Symmetry 2025, 17(8), 1367; https://doi.org/10.3390/sym17081367 - 21 Aug 2025
Viewed by 497
Abstract
In this study, we propose a novel hybrid metaheuristic framework that leverages symmetry awareness to enhance the performance of multi-objective optimization in renewable energy systems. Motivated by the repetitive and structured patterns commonly found in solar, wind, and demand profiles, we introduce a [...] Read more.
In this study, we propose a novel hybrid metaheuristic framework that leverages symmetry awareness to enhance the performance of multi-objective optimization in renewable energy systems. Motivated by the repetitive and structured patterns commonly found in solar, wind, and demand profiles, we introduce a symmetry-guided variant of the NSGA-II algorithm, enriched with a customized crossover operator that detects and exploits symmetrical patterns in candidate solutions. To further accelerate convergence and reduce computational cost, we integrate a surrogate modeling strategy using machine learning to approximate fitness evaluations in later generations. Our experimental evaluation, based on a synthetic dataset simulating one week (168 h) of operation in a hybrid solar–wind power system, incorporating realistic diurnal patterns in generation and demand, demonstrates the proposed method’s superiority over baseline NSGA-II in terms of solution diversity, convergence, and runtime efficiency. The results highlight the importance of integrating domain-specific structure—such as temporal symmetry—into the design of metaheuristics for sustainable energy applications. This approach opens new avenues for scalable, intelligent optimization in complex energy environments. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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27 pages, 2893 KB  
Article
Neural Network-Based Estimation of Gear Safety Factors from ISO-Based Simulations
by Moslem Molaie, Antonio Zippo and Francesco Pellicano
Symmetry 2025, 17(8), 1312; https://doi.org/10.3390/sym17081312 - 13 Aug 2025
Viewed by 394
Abstract
Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The [...] Read more.
Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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39 pages, 6764 KB  
Article
Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm
by Xiao Zhou, Jian Peng, Bowei Wen and Mingzhan Su
Symmetry 2024, 16(2), 159; https://doi.org/10.3390/sym16020159 - 29 Jan 2024
Cited by 7 | Viewed by 1887
Abstract
The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning [...] Read more.
The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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