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genetic algorithm optimization Design of Gradient Conformal Chiral Metamaterials and 3D Printing Verifiction for Morphing Wings

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Chinese Journal of Mechanical Engineering 2024年第6期37卷 346-364页

作者： Qian Zheng Weijun Zhu Quan Zhi Henglun Sun Dongsheng Li Xilun Ding School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China

This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil *** method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional *** optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing *** stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter *** outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.

关键词： Morphing wings Chiral metamaterials Gradient conformal design genetic algorithm optimization 3D printing

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Thermal design and genetic algorithm optimization of geothermal and solar-assisted multi-energy and hydrogen production using artificial neural networks

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ENERGY 2025年 324卷

作者： Yilmaz, Ceyhun Arslan, Muhammed Ozdemir, Safiye Nur Tokgoz, Nehir Sakarya Univ Dept Mech Engn Sakarya Turkiye Afyon Kocatepe Univ Motor Vehicles & Transportat Technol Afyonkarahisar Turkiye

A comprehensive thermodynamic and economic analysis of an innovative multiple energy and hydrogen production system that integrates solar and geothermal energy sources is presented in the study. The system is operated and optimized using a genetic algorithm (GA) based on an artificial neural network (ANN), which adapts in real-time and improves performance. The proposed system employs solar and geothermal energy to power a variety of processes, such as the generation of electricity, the production of hydrogen through electrolysis, and the heating of space. By optimizing the exergetic unit costs of hydrogen, heating, and electricity, we assess the economic feasibility of the system. The economic competitiveness of renewable energy sources is illustrated by the exergetic unit cost of 0.011 $/kWh (3.05 $/GJ) generated by the geothermal and solar-assisted power facility. The electrolysis unit generates hydrogen at a discharge rate of 0.0154 kg/s, with an exergetic unit cost of 1.491 $/kg H2 (12.42 $/GJ). This method is cost-effective for the production of pure fuel. In addition, the conversion of hydrogen to electricity by a fuel cell results in an optimized unit cost of electricity of 0.0778 $/kWh (21.61 $/GJ). The system also offers space heating at a unit heating cost of 0.005 $/kWh (1.38 $/GJ). The overall system's thermodynamic performance assessment indicates that the energy and exergy efficiencies are optimized at 34.5 % and 46 %, respectively. These results emphasize the integrated system's potential for sustainable and effective energy production, providing a prospective solution for various energy requirements while guaranteeing economic feasibility.

关键词： Artificial neural networks genetic algorithm optimization Geothermal energy Hydrogen production Solar energy Thermoeconomic analysis

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EffiDenseGenOp: Ensemble Transfer Learning With Hyperparameter Tuning Using genetic algorithm optimization for PCOS Detection From Ultrasound Sonography Images

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IEEE ACCESS 2025年 13卷 54285-54312页

作者： Ghosh, Ananya Srinivasan, Kathiravan Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 India

In this research, we present the revolutionary 'EffiDenseGenOp' framework for Polycystic Ovary Syndrome (PCOS) detection, leveraging the amalgamation of Ensembled Transfer Learning Models. By harnessing the synergies of Ensembled EfficientNetB7 and DenseNet201, our approach transcends conventional models, offering a robust solution for PCOS detection. Notably, we introduce a genetic algorithm-based hyperparameter tuning mechanism, optimizing the model configuration to ensure superior generalization and performance. Our contributions encompass a meticulous comparative analysis, pitting machine learning models, deep learning models, transfer learning models, and our proposed Ensembled Transfer Learning Models against each other. The ensemble technique strategically captures complementary patterns and features from each base model, significantly amplifying the overall predictive power. Moreover, we conduct a comprehensive exploration of hyperparameters, employing extensive tuning to enhance model performance and generalization. The efficiency of genetic algorithm is underscored through a rigorous comparative analysis. Additionally, a novel Fuzzy Inference System is introduced for image quality enhancement, designed after meticulous examinations of image behavior under varying noise levels and membership functions. Our model undergoes rigorous training through diverse image variations, employing data augmentation techniques and noise addition. Performance evaluation reveals superior accuracy (99.58%), precision (98.87%), recall (99.20%), F1-score (99.01%), and AUC-ROC score (98.97%), substantiated by detailed analyses including confusion matrices and AUC-ROC curves. Compared to existing models, our proposed model outperforms several state-of-the-art techniques, such as VGGNet16, PCOSNet, and ResNet50, with an accuracy of 99.95%, highlighting a significant improvement in PCOS detection performance. Robustness is ensured through exhaustive K-fold cross-validation, while v

关键词： Polycystic ovary syndrome transfer learning genetic algorithm optimization hyperparameter tuning Polycystic ovary syndrome transfer learning genetic algorithm optimization hyperparameter tuning

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Theme park greening VR design based on entertainment robots and genetic algorithm optimization: Digital entertainment design experience

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ENTERTAINMENT COMPUTING 2025年 52卷

作者： Huang, Yue Ke, Xinxia Jiang, Yulin Qingdao Univ Acad Fine Arts Qingdao 200071 Shandong Peoples R China Chizhou Univ Coll Arts & Educ Chizhou 247000 Anhui Peoples R China

Theme parks should provide attractive green design and digital interactive experiences to attract tourists and improve their satisfaction. Therefore, this study aims to achieve a digital interactive experience in theme park greening design through the use of entertainment robots and genetic algorithm optimization. The study analyzed the characteristics of experiential theme parks and introduced landscape 3D modeling techniques for creating interactive 3D models of green spaces in theme parks. By using technologies such as sensors and cameras, entertainment robots can perceive the presence and behavior of tourists. Entertainment robots can interact with tourists in real-time, customize them according to their interests and needs, and provide unique and enjoyable experiences for tourists. By collecting and analyzing feedback and behavioral data from tourists, the advantages and improvement points of digital interactive experiences can be identified. Through comparative experimental analysis with traditional experience methods, it was found that digital interactive experience can significantly improve tourist participation and satisfaction. Through user data analysis of interactive experience, this design can effectively improve the greening design of theme parks, enhance tourist participation and entertainment experience.

关键词： Entertainment robots genetic algorithm optimization Theme park greening design Digital interactive experience

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Automatic design of pixelated near-zero refractive index metamaterials based on elite-preserving genetic algorithm optimization

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RESULTS IN PHYSICS 2023年 48卷

作者： Sun, Shuo Jiang, Yannan Wang, Jiao Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China

Near-zero refractive index metamaterials (NZIM) have drawn a lot of attention recently for the development of high directivity antennas. However, their application has been constrained by the tedious and time-consuming manual design process. In this work, we offer an automated optimization design technique for NZIM that combines a modeling process based on pixelated metamaterials with an optimization process based on heuristic algorithms. The interactive CST-Python simulation is utilized for accomplishing the above automated design. In detail, the elite-preserving genetic algorithm (EGA) is specifically employed because of its improved capacity in locating the superior unit, and the fitness function proposed guarantees the performance of bandwidth and optimization process based on the principles of proportionate integration and penalty-like functions, respectively. Utilizing the suggested technique, we designed a NZIM unit that operates between 8.0 and 8.52 GHz. In order to validate the performance of the NZIM, a rectangular microstrip patch antenna (MPA) resonating at 8.2 GHz was employed as the radiation source for studies. According to the simulation results, the developed NZIM has a clearly beam focusing effect which causes the MPA's spherical waves to converge into quasi-plane waves. Furthermore, discussing the NZIM's application in high directivity antennas designing, the prototypes of the NZIM and MPA were fabricated and tested. The results revealed that the MPA's gain enhancement achieves 5.3 dB on average and 6.05 dB on maximum. Due to the automated optimization and design, this work is promised to advance NZIM's further applicability in high directivity antennas.

关键词： Near-zero refractive index metamaterial Pixelated metamaterial genetic algorithm optimization Automatic design High directivity antenna

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Kinetic parameters and heat of reaction for forest fuels based on genetic algorithm optimization

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THERMOCHIMICA ACTA 2022年 713卷

作者： Filho, G. C. Krieger Costa, Franklin Maria, G. F. Torraga Bufacchi, Paulo Trubachev, Stanislav Shundrina, Inna Korobeinichev, Oleg Univ Sao Paulo Sao Paulo Brazil Russian Acad Sci Siberian Branch Voevodsky Inst Chem Kinet & Combust Novosibirsk Russia Novosibirsk State Univ Novosibirsk Russia Russian Acad Sci Siberian Branch NN Vorozhtsov Novosibirsk Inst Organ Chem Novosibirsk Russia

Environmental issues and climate change are playing a central role in our society. Forest and peatland fires can prejudice the environment and population health. Therefore, it is vital to study this phenomenon. The main goal of this work is to determine the kinetic parameters and heat of reaction for different forest fuels. Pine needles and sphagnum peat are typical fuels from the Northern Hemisphere, while Amazonian leaves and high-density peat are from the Southern Hemisphere. The DTG curves for pine needles and Amazonian leaves clearly show two peaks of mass loss, while for sphagnum peat a third mass-loss peak occurs for higher temperatures. For high density peat degradation, there is only one significant peak of mass loss, which happens at a higher temperature. Also, the DSC curves for pine needles, Amazonian leaves, and high-density peat show two exothermic peaks along with the mass-loss peaks. However, the overall pattern is not the same. The maximum heat released for pine needles and high-density peat occurs at the second degradation stage. There are two exothermic peaks of almost the same magnitude for Amazonian leaves. Finally, three exothermic peaks match the peaks of mass loss for sphagnum peat. DTG and DSC curves patterns for all forest fuels are independent of the heating rate and atmospheric composition. In this work, the proposed pseudo reaction mechanism for pyrolysis and oxidation of the forest fuels contains five (for Amazonian leaves, high-density peat, and sphagnum peat) and seven (for pine needle) steps. The genetic algorithm optimization process compares the instantaneous recorded data of TGA, DTG, and DSC with the calculated ones. The optimized kinetic reactions parameters for the forest fuels are the activation energy, the pre-exponential factor, the global order of reaction, the stoichiometric coefficients, and the heat of reaction. The overall performance of the proposed mechanism is evaluated taking the error or the experimental data into

关键词： Forest fuels Kinetics parameters Heat of reaction TGA DSC genetic algorithm optimization

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Hybrid quantum search with genetic algorithm optimization

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2210-e2210页

作者： Ardelean, Sebastian Mihai Udrescu, Mihai Univ Politehn Timisoara Dept Comp & Informat Technol Timisoara Timis Romania

Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus improving their efficacy ( i.e. , quantum optimization of a classical algorithm). However, the extent of such improvements is still unclear. Conversely, Reduced Quantum genetic algorithm (RQGA) is a fully quantum algorithm that reduces the GA search for the best fitness in a population of potential solutions to running Grover's algorithm. Unfortunately, RQGA finds the best fitness value and its corresponding chromosome ( i.e. , the solution or one of the solutions of the problem) in exponential runtime, O(2n/2), n/2 ), where n is the number of qubits in the individuals' quantum register. This article introduces a novel QGA optimization strategy, namely a classical optimization of a fully quantum algorithm, to address the RQGA complexity problem. Accordingly, we control the complexity of the RQGA algorithm by selecting a limited number of qubits in the individuals' register and fixing the remaining ones as classical values of '0' and '1' with a genetic algorithm. We also improve the performance of RQGA by discarding unfit solutions and bounding the search only in the area of valid individuals. As a result, our Hybrid Quantum algorithm with genetic optimization (HQAGO) solves search problems in O(2 (n-k)/2 ) oracle queries, where k is the number of fixed classical bits in the individuals' register.

关键词： Quantum computing Quantum genetic algorithms genetic algorithm optimization Hybrid quantum genetic algorithm

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Tailoring the refractive index of ITO thin films, by genetic algorithm optimization of the reactive DC-sputtering parameters 8

Tailoring the refractive index of ITO thin films, by genetic...

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8th Conference on Oxide-Based Materials and Devices VIII

作者： Pour, Elnaz Afshari Shafai, Cyrus Univ Manitoba Winnipeg Dept Elect & Comp Engn 66 Chancellors Cir Winnipeg MB R3T 2N2 Canada

ISBN: (数字)9781510606524

ISBN: (纸本)9781510606517;9781510606524

The variation of oxygen concentration in the Indium Tin Oxide (ITO) structure highly impacts its electrical and optical characteristics. In this work, we investigated the effect of oxygen partial flow (O-2/O-2+Ar) and deposition pressure (p) on the refractive index (n) of reactive sputtered ITO thin films. A statistical study with a genetic algorithm (GA) optimization was implemented to find optimal deposition conditions for obtaining particular refractive indices. Several samples of ITO thin films with refractive indices ranging from 1.69 - 2.1 were deposited by DC sputtering technique at various oxygen concentrations and deposition pressures, in order to develop the statistical database. A linear polynomial surface was locally fitted to the data of O-2/O-2+Ar, p, and n of deposited films. This surface was then used as the fitness function of the GA. By defining the desired n as the objective value of the GA, the optimized deposition conditions can be found. Two cases were experimentally demonstrated, with the GA determining the needed process parameters to deposit ITO with n=2.2 and n=1.6. Measured results were very close to desired values, with n=2.25 and n=1.62, demonstrating the effectiveness of this method for predicting needed reactive sputtering conditions to enable arbitrary refractive indices.

关键词： Indium-Tin oxide refractive index of ITO genetic algorithm optimization ITO reactive DC sputtering

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genetic algorithm optimization of a CPW fed UWB circular ring monopole with modified ground plane

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2016年第6期58卷 1319-1323页

作者： Rodrigues, E. J. B. D'Assuncao, A. G. Lins, H. W. C. Peixeiro, C. Univ Fed Rio Grande do Norte Dept Commun Engn Caixa Postal 1655 BR-59072970 Natal RN Brazil Anatel Natl Agcy Telecommun BR-58043010 Joao Pessoa PB Brazil Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal

In this paper a coplanar waveguide (CPW) fed ultra-wideband (UWB) circular ring monopole antenna with a modified ground plane is proposed. The dimensions of the antenna are obtained using a genetic algorithm (GA) optimization procedure. To proof the concept and validate the design antenna prototypes have been fabricated and tested. A good agreement is obtained between numerical simulation and experimental results. The input reflection coefficient is below -10 dB in the frequency range 2.52 GHz to 12 GHz, which corresponds to an impedance bandwidth of about 4.8:1, and the gain is about 3 dBi. The proposed antenna element seems to be suitable for UWB and cognitive radio spectrum sensing applications. (c) 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:1319-1323, 2016

关键词： genetic algorithm optimization UWB monopole CPW feeding circular ring monopole modified ground plane cognitive radio

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genetic algorithm optimization for Water Curtain Cooling Fuzzy Control System

Genetic Algorithm Optimization for Water Curtain Cooling Fuz...

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International Conference on Fuzzy System and Data Mining (FSDM)

作者： Shi, Xiao-Ying Xu, Zhi-Bang Gong, Jing-Hong Yang, Hui East China Jiaotong Univ Acad Elect & Elect Engn Nanchang 330013 Peoples R China Nanchang Univ Nanchang 330013 Peoples R China

ISBN: (纸本)9781614996194;9781614996187

Intelligent optimization control combines intelligent optimization and intelligent control in order to optimize the local or global performance of control systems;it provides an effective way to solve control problems in complex systems. In this paper, a fuzzy logic controller is employed based on the genetic algorithm for control rules in a water curtain cooling system. Simulation results and industrial experiments indicate that fuzzy logic control based on the genetic algorithm optimization for the water curtain cooling process of accelerating steel is effective using this control model.

关键词： genetic algorithm optimization fuzzy reference rules fuzzy logic controller water curtain cooling system

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