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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 21301

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 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

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

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Published Papers (15 papers)

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Research

20 pages, 736 KB  
Article
Cognitive Biases in Asset Pricing: An Empirical Analysis of the Alphabet Effect and Ticker Fluency in the US Market
by Antonio Pagliaro
Symmetry 2026, 18(3), 477; https://doi.org/10.3390/sym18030477 - 11 Mar 2026
Viewed by 473
Abstract
Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces [...] Read more.
Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study (N=25, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is β=0.0036 (p=0.495) and the Alphabet Rank coefficient is β=−0.0027 (p=0.642), with the results robust to all tested parameterizations (λ∈[0.05,0.20]; p>0.50 throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower. 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, 7090 KB  
Article
SAW-Based Active Cleaning Cover Lens for Physical AI Optical Sensors
by Jiwoon Jeon, Jungwoo Yoon, Woochan Kim, Youngkwang Kim and Sangkug Chung
Symmetry 2026, 18(2), 347; https://doi.org/10.3390/sym18020347 - 13 Feb 2026
Cited by 1 | Viewed by 840
Abstract
This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks [...] Read more.
This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a single-phase unidirectional transducer (SPUDT) that intentionally breaks left–right symmetry through a geometrically asymmetric electrode array to generate SAW, thereby removing droplet contamination. First, the acoustic streaming induced inside a single sessile droplet by the SAW was visualized, and the dynamic behavior of the droplet upon SAW actuation was observed using a high-speed camera. The internal flow developed into a recirculating vortex structure with directional deflection relative to the SAW propagation direction, indicating a symmetry-broken streaming pattern rather than a purely symmetric circulation. Upon the application of the SAW, the droplet was confirmed to move a total of 7.2 mm along the SAW propagation direction, accompanied by interfacial deformation and oscillation. Next, an analysis of transport trajectories for five sessile droplets dispensed at different y-coordinates (y1–y5) revealed that all droplets were transported along the x-axis regardless of their initial positions. Furthermore, the analysis of transport velocity as a function of droplet viscosity (1 cP and 10 cP) and volume (2 μL, 4 μL, and 6 μL) demonstrated that the transport velocity gradually increased with driving voltage but decreased as viscosity increased under identical actuation conditions. Finally, the proposed cover lens was applied to an automotive front camera module to verify its effectiveness in improving object recognition performance by removing surface contamination. Based on its simple structure and driving principle, the proposed technology is deemed to be expandable as a surface contamination cleaning technology for various physical AI perception systems, including intelligent security cameras and drone camera lenses. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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15 pages, 1386 KB  
Article
Symmetry and Asymmetry Principles in Deep Speaker Verification Systems: Balancing Robustness and Discrimination Through Hybrid Neural Architectures
by Sundareswari Thiyagarajan and Deok-Hwan Kim
Symmetry 2026, 18(1), 121; https://doi.org/10.3390/sym18010121 - 8 Jan 2026
Viewed by 661
Abstract
Symmetry and asymmetry are foundational design principles in artificial intelligence, defining the balance between invariance and adaptability in multimodal learning systems. In audio-visual speaker verification, where speech and lip-motion features are jointly modeled to determine whether two utterances belong to the same individual, [...] Read more.
Symmetry and asymmetry are foundational design principles in artificial intelligence, defining the balance between invariance and adaptability in multimodal learning systems. In audio-visual speaker verification, where speech and lip-motion features are jointly modeled to determine whether two utterances belong to the same individual, these principles govern both fairness and discriminative power. In this work, we analyze how symmetry and asymmetry emerge within a gated-fusion architecture that integrates Time-Delay Neural Networks and Bidirectional Long Short-Term Memory encoders for speech, ResNet-based visual lip encoders, and a shared Conformer-based temporal backbone. Structural symmetry is preserved through weight-sharing across paired utterances and symmetric cosine-based scoring, ensuring verification consistency regardless of input order. In contrast, asymmetry is intentionally introduced through modality-dependent temporal encoding, multi-head attention pooling, and a learnable gating mechanism that dynamically re-weights the contribution of audio and visual streams at each timestep. This controlled asymmetry allows the model to rely on visual cues when speech is noisy, and conversely on speech when lip visibility is degraded, yielding adaptive robustness under cross-modal degradation. Experimental results demonstrate that combining symmetric embedding space design with adaptive asymmetric fusion significantly improves generalization, reducing Equal Error Rate (EER) to 3.419% on VoxCeleb-2 test dataset without sacrificing interpretability. The findings show that symmetry ensures stable and fair decision-making, while learnable asymmetry enables modality awareness together forming a principled foundation for next-generation audio-visual speaker verification systems. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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25 pages, 353 KB  
Article
Symmetry-Aware LLM-Driven Generation and Repair of Interactive Fiction Graphs in Twine/Twee
by Marcin Puchalski and Bożena Woźna-Szcześniak
Symmetry 2026, 18(1), 113; https://doi.org/10.3390/sym18010113 - 7 Jan 2026
Viewed by 1016
Abstract
We present a hybrid system that combines large language models (LLMs) with formal graph-analytic methods to generate and automatically repair interactive fiction (IF) stories written in the Twine/Twee format. We chronologically describe the practical challenges encountered when attempting to produce fully playable branching [...] Read more.
We present a hybrid system that combines large language models (LLMs) with formal graph-analytic methods to generate and automatically repair interactive fiction (IF) stories written in the Twine/Twee format. We chronologically describe the practical challenges encountered when attempting to produce fully playable branching narratives using contemporary state-of-the-art LLMs, including missing passages, trap-like cycles without exits, dead-end passages, narrative discontinuities, incorrect use of Twine macro commands, and inconsistent handling of story variables. To address these limitations, we deliberately abandon all macro- and variable-based logic and instead encode story state directly within passage names through structured, token-based naming. We formalize symmetry and asymmetry in the resulting narrative graphs: symmetrical convergence occurs when multiple branches with compatible states merge into a common passage, whereas asymmetry reveals incorrect or logically inconsistent merging of divergent states (for example, entering a scene in which an item or companion is present via paths where they were never acquired or met). We propose algorithms to detect naming-based asymmetries, cycles, unreachable endings, and structurally defective branches, and we integrate these diagnostics into a repair loop that prompts the LLM to rewrite missing or inconsistent parts of the story. Experiments with several LLM backends indicate that this approach can yield structurally robust and locally coherent interactive stories by reducing state inconsistencies and structural defects. Beyond the specific case of Twine, we argue that symmetry/asymmetry analysis offers a powerful lens for evaluating and correcting AI-generated narrative graphs in general. 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, 605 KB  
Article
A Biased-Randomized Algorithm for the Bi-Objective Capacitated Dispersion Problem with Symmetries
by Juan F. Gomez, Wenwen Chen, Laura Calvet, Majsa Ammouriova and Angel A. Juan
Symmetry 2026, 18(1), 110; https://doi.org/10.3390/sym18010110 - 7 Jan 2026
Viewed by 376
Abstract
Given a network of nodes and a certain demand that needs to be satisfied, the capacitated dispersion problem (CDP) involves selecting a subset of nodes to maximize dispersion between them. In many practical instances, symmetry in the structure of the selected nodes (e.g., [...] Read more.
Given a network of nodes and a certain demand that needs to be satisfied, the capacitated dispersion problem (CDP) involves selecting a subset of nodes to maximize dispersion between them. In many practical instances, symmetry in the structure of the selected nodes (e.g., using nodes of the same type) can lead to synergies. Hence, this paper studies a bi-objective variant of the CDP to account for these symmetries. The first goal seeks to maximize the minimum distance between opened nodes, while the second goal accounts for symmetry by penalizing the use of nodes of different types (in our case, represented by different colors). We formalize the problem as a bi-objective mathematical program and address it through a classical multiobjective strategy, the ϵ-constraint method. Exact methods are used when the problem size allows, even though the problem is NP-hard. To tackle larger instances, we design a biased-randomized algorithm based on a constructive heuristic. Computational experiments show that our biased-randomized algorithm provides high-quality approximations of the Pareto frontier. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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23 pages, 1934 KB  
Article
Asymmetric Feature Weighting for Diversity-Enhanced Random Forests
by Ye Eun Kim, Seoung Yun Kim and Hyunjoong Kim
Symmetry 2026, 18(1), 73; https://doi.org/10.3390/sym18010073 - 1 Jan 2026
Viewed by 654
Abstract
Random Forest (RF) is one of the most widely used ensemble learning algorithms for classification and regression tasks. Its performance, however, depends not only on the accuracy of individual trees but also on the diversity among them. This study proposes a novel ensemble [...] Read more.
Random Forest (RF) is one of the most widely used ensemble learning algorithms for classification and regression tasks. Its performance, however, depends not only on the accuracy of individual trees but also on the diversity among them. This study proposes a novel ensemble method, Heterogeneous Random Forest (HRF), which enhances ensemble diversity through adaptive and asymmetric feature weighting. Unlike conventional RF that treats all features equally during tree construction, HRF dynamically reduces the sampling probability of features that have been frequently selected—particularly those appearing near the root nodes of previous trees. This mechanism discourages repetitive feature usage and encourages a more balanced and heterogeneous ensemble structure. Simulation studies demonstrate that HRF effectively mitigates feature selection bias, increases structural diversity, and improves classification accuracy, particularly on datasets with low noise ratios and diverse feature cardinalities. Comprehensive experiments on 52 benchmark datasets further confirm that HRF achieves the highest overall performance and significant accuracy gains compared to standard ensemble methods. These results highlight that asymmetric feature weighting provides a simple yet powerful mechanism for promoting diversity and enhancing generalization in ensemble learning. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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28 pages, 9186 KB  
Article
Artificial Neural Network-Based Optimization of an Inlet Perforated Distributor Plate for Uniform Coolant Entry in 10 kWh 24S24P Cylindrical Battery Module
by Tai Duc Le, You-Ma Bang, Nghia-Huu Nguyen and Moo-Yeon Lee
Symmetry 2026, 18(1), 14; https://doi.org/10.3390/sym18010014 - 21 Dec 2025
Cited by 4 | Viewed by 789
Abstract
In this study, a multi-objective optimization framework based on an artificial neural network (ANN) was developed for an inlet perforated distributor plate in a 24S24P 10 kWh cylindrical lithium-ion battery module using immersion cooling. A combined Newman, Tiedeman, Gu and Kim with Computational [...] Read more.
In this study, a multi-objective optimization framework based on an artificial neural network (ANN) was developed for an inlet perforated distributor plate in a 24S24P 10 kWh cylindrical lithium-ion battery module using immersion cooling. A combined Newman, Tiedeman, Gu and Kim with Computational Fluid Dynamics (NTGK-CFD) model was used to generate a symmetrically designed space by varying the input variables, including hole size A (mm), hole spacing ΔH (mm), and coolant mass flow rate Vin (kg/s). A three-level full factorial design was used to generate 27 cases, then CFD simulations were performed to provide a training data for the ANN model to predict the output variables, including maximum temperature Tmax, maximum temperature difference ΔTmax, and pressure drop ΔP. The results show that the ANN model provides a reliable predictive model, capable of reproducing the thermal-hydraulic behavior of the immersion-cooled battery module with high fidelity via correlation coefficients R of 0.997 for all three output variables. In addition, Pareto-based optimization shows designs that balance cooling efficiency and pumping power. The selected optimal solution maintains Tmax within the optimal range at 37.97 °C while reducing ΔP by up to 44%, providing a practical solution for large-scale battery module thermal management in EVs. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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19 pages, 8611 KB  
Article
Pixel-Level Fuzzy Rule Attention Maps for Interpretable MRI Classification
by Tae-Wan Kim and Keun-Chang Kwak
Symmetry 2025, 17(12), 2187; https://doi.org/10.3390/sym17122187 - 18 Dec 2025
Viewed by 542
Abstract
Although Artificial Intelligence (AI) has achieved notable performance, particularly in medicine, the structural opacity leading to the black-box phenomenon inhibits interpretability, thus necessitating a balance (Symmetry) between performance and transparency. Specifically, in the medical domain, effective diagnosis requires that high predictive performance be [...] Read more.
Although Artificial Intelligence (AI) has achieved notable performance, particularly in medicine, the structural opacity leading to the black-box phenomenon inhibits interpretability, thus necessitating a balance (Symmetry) between performance and transparency. Specifically, in the medical domain, effective diagnosis requires that high predictive performance be symmetrically counterbalanced by sufficient trust and explainability for clinical practice. Existing visualization techniques like Grad-CAM can highlight attention regions but provide limited insight into the reasoning process and often focus on irrelevant areas. To address this limitation, we propose a Fuzzy Attention Rule (FAR) model that extends fuzzy inference to MRI (Magnetic Resonance Imaging) image classification. The FAR model applies pixel-level fuzzy membership functions and logical operations (AND, OR, AND + OR, AND × OR) to generate rule-based attention maps, enabling explainable and convolution-free feature extraction. Experiments on Kaggle’s Brain MRI and Alzheimer’s MRI datasets show that FAR achieves comparable accuracy to Resnet50 while using far fewer parameters and significantly outperforming MLP. Quantitative and qualitative analyses confirm that FAR focuses more precisely on lesion regions than Grad-CAM. These results demonstrate that fuzzy logic can enhance both the explainability and reliability of medical AI systems without compromising performance. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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20 pages, 569 KB  
Article
Symmetry-Preserving Optimization of Differentially Private Machine Learning Based on Feature Importance
by Nan-I Wu, Jing-Ting Wu and Min-Shiang Hwang
Symmetry 2025, 17(10), 1747; https://doi.org/10.3390/sym17101747 - 16 Oct 2025
Viewed by 883
Abstract
Symmetry plays a critical role in preserving the structural balance and statistical integrity of datasets, particularly in privacy-preserving machine learning. Differential privacy introduces random noise to individual data points to ensure privacy while maintaining the overall symmetry of statistical distributions. However, excessive noise [...] Read more.
Symmetry plays a critical role in preserving the structural balance and statistical integrity of datasets, particularly in privacy-preserving machine learning. Differential privacy introduces random noise to individual data points to ensure privacy while maintaining the overall symmetry of statistical distributions. However, excessive noise can reduce the utility of data, model accuracy, and computational efficiency. This study proposes a symmetry-preserving optimization framework for differentially private machine learning by integrating feature importance and t-SNE (t-distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), and PCA (Principal Component Analysis), respectively. Feature importance derived from a random forest selects high-value features to improve data relevance. At the same time, t-SNE preserves geometric symmetry by retaining local and global structures more effectively than PCA or UMAP. Therefore, t-SNE is the best feature extraction method for dimensionality reduction in the proposed scheme. Experimental results demonstrate that the t-SNE method significantly enhances model performance under differential privacy, showing improved accuracy and reduced computational time compared to PCA and UMAP while preserving the underlying symmetry of the data distributions. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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16 pages, 2694 KB  
Article
Leveraging Hierarchical Asymmetry for Efficient Resource Discovery in Message Queuing Telemetry Transport
by Hung-Yu Chien, An-Tong Shih and Yuh-Ming Huang
Symmetry 2025, 17(10), 1722; https://doi.org/10.3390/sym17101722 - 13 Oct 2025
Viewed by 842
Abstract
With the rapid growth of the Internet of Things, efficient resource discovery has become essential for effective resource management. Although Message Queuing Telemetry Transport is one of the most widely adopted IoT communication protocols, it lacks a native resource discovery mechanism or any [...] Read more.
With the rapid growth of the Internet of Things, efficient resource discovery has become essential for effective resource management. Although Message Queuing Telemetry Transport is one of the most widely adopted IoT communication protocols, it lacks a native resource discovery mechanism or any resource discovery standards. The existing Message Queuing Telemetry Transport resource discovery relies on symmetric full-mesh synchronization, which causes excessive traffic and unacceptable latency as the system scales up: this restricts its use to only small-size deployments. To overcome these limitations, this paper proposes a Hierarchical Message Queuing Telemetry Transport resource discovery and distribution framework, inspired by the hierarchical design of the Domain Name System. By introducing hierarchical asymmetry, the framework reduces communication overhead, enhances scalability, and maintains efficient real-time query performance, as demonstrated by implementation and simulation results. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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30 pages, 987 KB  
Article
Combining Constructed Artificial Neural Networks with Parameter Constraint Techniques to Achieve Better Generalization Properties
by Ioannis G. Tsoulos, Vasileios Charilogis and Dimitrios Tsalikakis
Symmetry 2025, 17(9), 1557; https://doi.org/10.3390/sym17091557 - 17 Sep 2025
Viewed by 966
Abstract
This study presents a novel hybrid approach combining grammatical evolution with constrained genetic algorithms to overcome key limitations in automated neural network design. The proposed method addresses two critical challenges: the tendency of grammatical evolution to converge to suboptimal architectures due to local [...] Read more.
This study presents a novel hybrid approach combining grammatical evolution with constrained genetic algorithms to overcome key limitations in automated neural network design. The proposed method addresses two critical challenges: the tendency of grammatical evolution to converge to suboptimal architectures due to local optima, and the common overfitting problems in evolved networks. Our solution employs grammatical evolution for initial architecture generation while implementing a specialized genetic algorithm that simultaneously optimizes network parameters within dynamically adjusted bounds. The genetic component incorporates innovative penalty mechanisms in its fitness function to control neuron activation patterns and prevent overfitting. Comprehensive testing across 53 diverse datasets shows our method achieves superior performance compared to traditional optimization techniques, with an average classification error of 21.18% vs. 36.45% for ADAM, while maintaining better generalization capabilities. The constrained optimization approach proves particularly effective in preventing premature convergence, and the penalty system successfully mitigates overfitting even in complex, high-dimensional problems. Statistical validation confirms these improvements are significant (p < 1.1×10−8) and consistent across multiple domains, including medical diagnosis, financial prediction, and physical system modeling. This work provides a robust framework for automated neural network construction that balances architectural innovation with parameter optimization while addressing fundamental challenges in evolutionary machine learning. Full article
(This article belongs to the Special Issue Applications of Symmetry/Asymmetry in Artificial Intelligence and Deep Metaheuristics)
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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
Cited by 7 | Viewed by 3116
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
Cited by 5 | Viewed by 3609
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
Cited by 3 | Viewed by 1783
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 2617
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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