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Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

NEURAL COMPUTING & APPLICATIONS 2020年第13期32卷 9383-9425页

作者： Zhao, Weiguo Wang, Liying Zhang, Zhenxing Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056021 Hebei Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL 61820 USA

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at.

关键词： Artificial ecosystem-based optimization Global optimization Constrained problems optimization algorithm Engineering design Hydrogeological parameter

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Framework for optimization of two-element airfoil using Nvidia Modulus, a physics informed neural network solver

Framework for optimization of two-element airfoil using Nvid...

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AIAA SciTech Forum

作者： Tay, Wee-Beng Liao-Yang, Tian-Chun Luo, Hong-Rui Chen, Kai-Peng Khoo, Boo-Cheong Natl Univ Singapore Temasek Labs 5A Engn Dr 102-02 Singapore 117411 Singapore Natl Univ Singapore Dept Mech Engn 5A Engn Dr 106-92 Singapore 117411 Singapore Natl Univ Singapore Dept Mech Engn 5A Engn Dr 106-90 91 Singapore 117411 Singapore Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117575 Singapore

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

A framework has been designed to optimize the performance of two-element airfoils using an open-source physics informed neural network solver under certain aerodynamics constraints, The solver used is the Nvidia Modulus. The objective function is constructed such that with a single training, it can predict the flow fields and force coefficients for a range of angles of attack and airfoil thickness. optimization is then performed using the trained network to improve the airfoil performance. Results show that this trained network can predict the flow fields and force coefficients with reasonable accuracy much faster than traditional computational fluid dynamics solvers. When coupled with an optimization routine, it can also predict maximum cl, cl/cd and endurance coefficient.

关键词： Multi Element Airfoils Artificial Neural Network Computational Fluid Dynamics Lift Coefficient Aerodynamic Design optimization optimization algorithm Python Bayesian optimization Black Box Message Passing Interface

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Retrieval and Analysis of Sea Surface Salinity in Coastal Waters Using Satellite Data Based on IGWO-BPNN: A Case Study of Qinzhou Bay, Guangxi, China

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WATER 2025年第1期17卷

作者： Zhong, Maoyuan Yao, Huanmei Liu, Yin Qiao, Junchao Chen, Meijun Zhong, Weiping Guangxi Univ Sch Resources Environm & Mat Nanning 530004 Peoples R China Guangxi Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Environm Protect Nanning 530004 Peoples R China Marine Environm Monitoring Ctr Guangxi Zhuang Auto Marine Ecol Environm Guangxi Field Sci Observat Re Beihai 536000 Peoples R China

This study proposes a high-precision method for retrieving sea surface salinity (SSS) using GF-1 satellite imagery, focusing on Qinzhou Bay along the Guangxi coast. The analysis identified the spectral index B3xB4/(B1xB2) as having the strongest correlation with SSS (R = 0.929). To enhance the performance of the Back Propagation Neural Network (BPNN) model, optimization algorithms including Improved Grey Wolf optimization (IGWO), Particle Swarm optimization (PSO), and White Shark optimization (WSO) were applied. Comparative results show that IGWO significantly optimized network weights and thresholds, yielding superior test performance metrics (MAE = 0.906 psu, MAPE = 4.124%, RMSE = 1.067 psu, and R2 = 0.953), demonstrating strong generalization ability. Validation using third-party data indicated accuracy reductions of 10.9% and 8.6% in Qinzhou Bay and Tieshan Port, respectively, highlighting the model's robustness and broad applicability. SSS retrieval results for Qinzhou Bay in 2023 revealed significant spatial and seasonal variations: the Inner Bay exhibited lower salinity (average 14 psu) from April to September due to freshwater inflows, while salinity increased (average 22 psu) from November to February. The Outer Bay, influenced by its connection to the South China Sea, maintained consistently high salinity levels (25-30 psu) year-round. Additionally, different models showed varying levels of effectiveness in Qinzhou Bay's complex salinity environment;the IGWO-BPNN model, with its dynamic weight adjustment mechanism, demonstrated superior adaptability in areas with high salinity variability, outperforming other models. These findings suggest that the IGWO-BPNN model provides high accuracy and stability, supporting real-time, precise monitoring in Qinzhou Bay and similar coastal waters, thereby offering robust support for water quality management and marine conservation.

关键词： Qinzhou Bay sea surface salinity BPNN GF-1 satellite optimization algorithm Improved Grey Wolf optimization

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Design, fabrication and measurement of Terahertz flexible broadband beam splitter

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OPTICS AND LASER TECHNOLOGY 2025年 184卷

作者： Teng, Yan Li, Chun Xie, Guoao Huang, Zhengwei Yao, Haiyun Zhang, Yonggang Liang, Lanju Ren, Yuan Jiang, Ling Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Chinese Acad Sci Purple Mt Observ Nanjing 210033 Peoples R China Zaozhuang Univ Zaozhuang 277160 Peoples R China Anhui Univ Sci & Technol Sch Elect & Informat Engn Huainan 232001 Anhui Peoples R China

Beam splitters are an important part of optical systems and have a wide range of applications in wireless communications and target detection. In this study, bendable structures based on subwavelength nanostructured metasurfaces with an arbitrary number of beams are constructed. To achieve multiple beams, the phase distribution of a multi-beam splitting metasurface in the 2D direction is calculated directly by an optimized phase recovery algorithm. The cells exhibiting high polarization conversion efficiency are arranged periodically according to the discrete phase. By full-wave simulation, the beam-splitting metasurfaces realize 2 x 2, 4 x 4, and 6 x 6 main beams with high power efficiency and homogeneity from 0.2 THz to 0.37 THz. In addition, the effect of metasurface bending on beam splitting is further investigated using flexible materials that can be fitted into more devices. It is demonstrated that the performance remains optimal at curvatures of less than 1/10,000 mu m-1. Due to the 2D structure, this beam splitter allows electromagnetic waves to be incident at an angle of more than 40 degrees for more demanding applications. Finally, a flexible beam splitter fabrication method based on commercially available polyimide films is proposed and measured, and the results are in agreement with the simulation results. Compared to other beam splitters, the flexible metasurface is fabricated in this paper to reduce the cost, is suitable for large-scale production, and is easy to integrate. These devices can be used in THz optical systems, as well as in mobile applications.

关键词： Multi-Beam Splitter Terahertz optimization algorithm Phase Recovery Flexible Material

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Efficient Automatic Design of IGBT Structural Parameters Using Differential Evolution and Machine Learning Model

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第3期44卷 1059-1069页

作者： Yao, Qing Chen, Jing Yang, Kemeng Yao, Jiafei Zhang, Jun Dai, Yuxuan Tang, Weihua Zhang, Bo Guo, Yufeng Nanjing Univ Posts & Telecommun Coll Integrated Circuit Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Natl & Local Joint Engn Lab RF Integrat & Micropac Nanjing 210023 Peoples R China Univ Elect Sci & Technol China State Key Lab Elect Thin Films & Integrated Device Chengdu 610054 Peoples R China

Insulated gate bipolar transistors (IGBTs) are the key component in power electronics, and the intricate relationship between their performance and structural parameters poses a formidable challenge in the design process. This article proposes an automatic optimal design method for IGBT structural parameters to leverage the pretrained machine learning (ML) model to efficiently predict the initial IGBT device's performance, followed by utilizing the differential evolution (DE) algorithm to automatically adjust structural parameters based on the disparity between predicted and expected device performance until the expected performance is achieved. The method is validated in the design of punch-through IGBTs (PT-IGBTs) and trench gate field-stop IGBTs (FS-IGBTs), and the performance of technology computer-aided design (TCAD) simulation of the designed device is similar to the target performance. In particular, the simulation results of the designed FS-IGBT are highly fitted to the datasheet of the commercial device, which verifies the generalizability and effectiveness of the method. In addition, comparative analyses with various algorithms show DE provides the fastest optimization and extraordinary robustness under the exact specifications. Crucially, the proposed design scheme aligns with semiconductor physics. The method simplifies IGBT design without the need for manual tuning and TCAD tool simulation.

关键词： Insulated gate bipolar transistors Structural engineering Performance evaluation Predictive models optimization Training Prediction algorithms Artificial neural networks (ANNs) automatic optimization electrical performance insulated gate bipolar transistor (IGBT) optimization algorithm

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A Modified U-Shaped Transfer Function: Applied to Classify Parkinson'S Disease

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COMPUTATIONAL INTELLIGENCE 2025年第2期41卷

作者： Sharma, Suvita Rani Singh, Birmohan Kaur, Manpreet St Longowal Inst Engn & Technol Dept Comp Sci & Engn Sangrur Punjab India St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Sangrur Punjab India

Transfer functions have a very important role in metaheuristic optimization-based feature selection algorithms as these functions map the continuous search space into binary space. The U-shaped transfer function (UTF) is one of the transfer functions used to solve the problem of feature selection. However, the UTF requires the selection of parametric values, which can vary for different types of data. To address this issue, an approach to select the parameters of the UTF has been proposed based on a time-varying adaption method, resulting in the modified U-shaped transfer function (MUTF). Furthermore, a methodology has been proposed to enhance feature selection and classification for Parkinson's disease by utilizing z-score normalization in conjunction with a modified U-shaped transfer function and the binary self-adaptive bald eagle search (MUTF-SABES) optimization algorithm. The z-score normalization has been used to mitigate issues caused by outliers. Also, the performance of the k nearest neighbor classifier is improved by selecting an optimal parameter value using the proposed MUTF-SABES algorithm. The effectiveness of the proposed methodology is validated on seven different Parkinson's disease datasets and compared with five state-of-the-art optimization algorithms: Salp Swarm algorithm, Harris Hawks optimization, equilibrium optimizer, aquilla optimizer, and Honey Badger algorithm, to evaluate its performance superiority. The results achieved using the proposed approach have been superior or analogous to the erstwhile algorithms for performance comparability. Friedman's mean rank test is used to check the statistical significance of the propounded approach. The lowest Friedman's mean rank value obtained using the proposed approach indicates that the proposed approach has the potential to become an alternative to other well-known strategies.

关键词： classification feature selection normalization optimization algorithm Parkinson's disease

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Simultaneous Design and Trajectory optimization for Boosted Hypersonic Glide Vehicles

Simultaneous Design and Trajectory Optimization for Boosted ...

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AIAA SciTech Forum

作者： Cangelosi, Jonathan R. Heinkenschloss, Matthias Needels, Jacob T. Alonso, Juan J. Rice Univ Dept Computat Appl Math & Operat Res Houston TX 77005 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This manuscript describes a methodology for simultaneous vehicle and trajectory optimization of a hypersonic glide vehicle. The co-design problem is formulated as an optimization problem with constraints including vehicle dynamics, path constraints (e.g., surface heating), and other constraints. The discretized optimization problem is solved simultaneously in the vehicle design parameters, the state variables, and the controls using an interior point method. Gaussian process (GP) surrogates, which are generated from sample candidate designs and flight conditions, are used to model vehicle aerodynamic performance and mass properties, as well as their first and second-order derivatives required by the optimizer. These GP surrogates and their derivatives are computationally inexpensive, making the all-at-once optimization approach for the co-design problem more tractable. To mitigate the effect of surrogate model errors on the solution of the optimal control problem, the GP models are refined using samples of the vehicle aerodynamic performance and mass properties at the solution of the co-design problem with the current surrogate. The resulting framework is applied to maximizing the range of a hypersonic glide vehicle with path and terminal constraints. Possible extensions of this methodology are also discussed, including the incorporation of more complex vehicle models such as multi-fidelity models, as well as adaptive surrogate modeling strategies to mitigate the effect of model errors on the solution of the optimal control problem.

关键词： Hypersonic Glide Vehicle Optimal Control Problem Aerodynamic Performance Reduced Order Modelling optimization algorithm Gaussian Process Aerodynamic Force Coefficients Dynamic Pressure Thermal Protection System Lift to Drag Ratio

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A Global optimization Method for Wideband and Small Supergain Arrays Design Using Artificial Neural Network

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2023年 4卷 1016-1028页

作者： Touhami, Abdellah Collardey, Sylvain Sharaiha, Ala Univ Rennes Inst Elect & Technol NumeR CNRS UMR 6164 F-35000 Rennes France

This paper introduces an efficient approach for compact, wideband and supergain arrays design using artificial neural network (ANN) based optimization. The proposed method optimize at the same time the distance inter-elements of the array antenna, its input impedance as well as its directivity. Such global optimization considerably improves the performances of superdirective arrays in terms of gain, bandwidth and efficiency. The proposed method is used afterwards to synthesize a three-elements array using two different unit elements. The comparison of the achieved performances and the Harrington limit reveals that the developed antennas can be qualified as supergain antennas. To our knowledge, this is the first demonstration of a wideband supergain array with more than two elements in the open literature. To validate the results, a prototype was manufactured and measured. The measurements show that the antenna has a wide impedance bandwidth of 22.6%, a peak directivity of 8.3 dBi and a total efficiency greater than 80%.

关键词： Superdirective array supergain array parasitic array directivity artificial neural network optimization algorithm

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Optimizing energy hubs with a focus on ice energy storage: a strategic approach for managing cooling, thermal, and electrical loads via an advanced slime mold algorithm

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 2568-2579页

作者： Hai, Tao Shami, Hayder Oleiwi Saleh, Sami Abdulhak Agarwal, Diwakar Rajab, Husam Mahmood, Ahmed Mohammed Hussein, Abbas Hameed Abdul Hasan, Dheyaa Flayih Mushtaq, Hiba Singh, Narinderjit Singh Sawaran Nanchang Inst Sci & Technol Sch Informat & Artificial Intelligence Nanchang 330108 Peoples R China Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Guizhou Peoples R China INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Al Amarah Univ Coll Dept Accounting Amarah Maysan Iraq Univ Misan Coll Adm & Econ Dept Econ Amarah Iraq Al Amarah Univ Coll Engn Tech Mech Power Dept Amarah Maysan Iraq GLA Univ Inst Engn & Technol Dept Elect & Commun Engn Mathura India Najran Univ Coll Engn Dept Mech Engn King Abdulaziz RdPOB 1988 Najran Saudi Arabia Alnoor Univ Hlth & Med Tech Coll Dept Opt Tech Mosul Iraq Ahl Al Bayt Univ Karbala Iraq Natl Univ Sci & Technol Coll Hlth & Med Technol Dhi Qar Iraq Gilgamesh Ahliya Univ Baghdad Iraq

Amidst the increasing incorporation of multicarrier energy systems in the industrial sector, this article presents a detailed stochastic methodology for the optimal operation and daily planning of an integrated energy system that includes renewable energy sources, adaptive cooling, heating, and electrical loads, along with ice storage capabilities. To address this problem, it applies the 2 m + 1 point estimation method to accurately assess system uncertainties while minimizing computational complexity. The "2 m + 1 point" technique swiftly evaluates unpredictability through Taylor series calculations, capturing deviations in green energy output, and the demand for both electric and thermal energy across power networks, while also considering the oscillating costs associated with senior energy transmission systems. In addition, this article proposes a novel self-adaptive optimization technique, called the enhanced self-adaptive mucilaginous fungus optimization algorithm (SMSMA), dedicated to overcoming the intricate nonlinear challenges inherent in the optimal daily operation of an energy system. The advanced self-adaptive strategy relies on wavelet theory to enhance the capability and effectiveness of the original mucilaginous fungus algorithm in optimizing daily schedules for an integrated energy system. Numerical analyses demonstrate that the introduced stochastic daily scheduling framework, coupled with the SMSMA optimization algorithm, effectively reduces the operating costs of the energy system.

关键词： multicarrier energy spheres stochastic framework renewable energy sources optimization algorithm operating cost

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Cross-Entropy Method for Laser Defense Applications

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第1期22卷 53-58页

作者： Hanak, J. Novak, J. Ben-Asher, J. Z. Chudy, P. Brno Univ Technol Fac Informat Technol Brno 61200 Czech Republic Technion Israel Inst Technol Dept Aerospace Engn IL-32000 Haifa Israel Univ Wurzburg Inst Comp Sci D-97074 Wurzburg Germany

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