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Design of High Gain Metasurface Antenna Using Hybrid African Vulture's optimization and Capuchin Search algorithm for RF Energy Harvesting (Jul, 10.1007/s11277-023-10583-1, 2023)

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WIRELESS PERSONAL COMMUNICATIONS 2023年第1期132卷 95-95页

作者： Kumar, M. Vinod Sharma, Dinesh Sri Vasavi Engn Coll Tadepalligudem 534101 Andhra Pradesh India Panjab Univ CCET Chandigarh Dept ECE Chandigarh 160019 India

In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhance the antenna design, a high-gain MSA is designed using the hybrid African Vulture'sa* optimization algorithm (AVOA), and the Capuchin Search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_a* optimization algorithm \times 1.25\lambda_a* optimization algorithm \times 0.02\lambda_a* optimization algorithm\) with a resonating frequency of 5 GHz. To design the high gain MSA, the proposed Hybrid African Vulture’s optimization and Capuchin Search algorithm (Hyb-AVOA-CapSA) is used to enhance the antenna parameters such as radiation efficiency, Bandwidth, gain, and return loss. Therefore, the proposed MSA design has achieved high efficiency and profit. Finally, the simulation has done on HFSS19 and ADS2020 version software; and evaluated using MATLAB. The proposed antenna gives a better efficiency of 70.12%, and resonate at 1.5 GHz of the axial ratio bandwidth at 5 GHz resonant frequency. The gain of the proposed antenna has increased from 6.86 to 7.6 dBi. While examining the comparative outcomes, the proposed approach has attained 22.4%, 23.7% high gain, and 18.85%, 12.6% lower return loss than the compared methods. Thus, the designed MSA is applied in RF energy harvesting applications because of its compact, low-profile, and simple structure. The Rectenna design uses a voltage doubler circuit at the receiver end and produces 5.55 V.

关键词： African vulture’s optimization algorithm Capuchin search algorithm Gain Metasurface antenna RF energy harvesting

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

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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-heuristica* 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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optimization of automotive battery pack casing based on equilibrium response surface model and multi-objective particle swarm algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第6期237卷 1183-1194页

作者： Liu, Fei Xu, Yalong Li, Meng Guo, Jiale Han, Bing Shanghai Univ Engn Sci Sch Mech & Automot Engn 333 Long Teng Rd Shanghai 201620 Peoples R China

Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarma* optimization algorithm, this paper proposes an optimization design method for lightweight of battery pack shell. The thickness of the battery pack shell is the optimization parameter. The stress, strain, and frequency of the battery pack shell under typical working conditions are used as boundary conditions. The response surface model is established according to the criterion of cross terms in adaptive response surface method, and the multi-objective particle swarma* optimization algorithm is used for iterative solution. The optimization results show that the maximum stress of the battery pack is reduced to the appropriate range, the first-order frequency is increased by 41% to reduce resonance, the maximum deformation is reduced from 2.7 to 1.12 mm, and the total mass is reduced by 26.8%. The battery pack optimization design method proposed in this paper can achieve lightweighting while meeting safety performance.

关键词： Battery pack casing mechanical performance adaptive response surface lightweight energy saving optimization algorithm

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Short-Term Wind Power Prediction Based On Particle Swarm optimization-Extreme Learning Machine Model Combined With Adaboost algorithm

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IEEE ACCESS 2021年 9卷 94040-94052页

作者： An, Guoqing Jiang, Ziyao Cao, Xin Liang, Yufei Zhao, Yuyang Li, Zheng Dong, Weichao Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China Hebei Engn Lab Wind Power Photovolta Coupling Hyd Shijiazhuang 050018 Hebei Peoples R China Hebei Construct & Investment Grp New Energy Co Lt Shijiazhuang 050051 Hebei Peoples R China

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

关键词： Adaboost algorithm extreme learning machine optimization algorithm wind power prediction

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Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

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JOURNAL OF MODELLING IN MANAGEMENT 2025年第3期20卷 1030-1080页

作者： Farahani, Milad Shahvaroughi Babaei, Shiva Kharazan, Zahra Sadat Bai, Ali Rahmati, Zahra Ghasemi, Ghazal Alipour, Fardin Farrokhi-Asl, Hamed Khatam Univ Dept Finance Tehran Iran Univ Northern Iowa Coll Business Cedar Falls IA USA Univ Wisconsin Milwaukee Lubar Coll Business Milwaukee WI USA Islamic Azad Univ Dept Law Shahreza Iran Khatam Univ Dept Finance Tehran Iran Univ Wisconsin Milwaukee Cofrin Sch Business Green Bay WI USA

Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between cryptoprice and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings - The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value - The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies' prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

关键词： Cryptocurrency Artificial intelligence optimization algorithm Econometric methods Curve-fitting

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NEST: A Quadruple-Node Upset Recovery Latch Design and algorithm-Based Recovery optimization

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4590-4600页

作者： Huang, Zhengfeng Sun, Liting Wang, Xu Liang, Huaguo Lu, Yingchun Yan, Aibin Pan, Jun Wen, Xiaoqing HeFei Univ Technol Sch Microelect Hefei 230601 Peoples R China Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka 8208502 Japan

Multinode upset induced by radiation on integrated circuits has caused many circuit reliability issues. This article proposes a single-event quadruple-node upset (QNU) recovery latch (NEST), based on four circular feedback loops that are formed by 25 C-elements to realize high robustness. NEST achieves 29.02% reduction in power consumption compared to the latch design and algorithm-based verification protected against multiple-node upset (LDAVPM) latch and 51.44% reduction in setup time compared to the quadruple-node upset recoverable and high-impedance-state insensitive latch (QRHIL) latch. NEST also achieves a 99.29% QNU recovery rate. Furthermore, a high-speed, high-precisiona* optimization algorithm for multinode upset recovery is also proposed and implemented. This algorithm achieves 99.84 reduction in simulation time for exhaustive fault injections having equivalent accuracy with high performance simulation program with integrated circuit emphasis (HSPICE).

关键词： Latches Power demand Logic gates Inverters Radiation hardening (electronics) optimization Aerospace and electronic systems optimization algorithm quadruple-node upset (QNU) recovery radiation hardening by design recovery rate

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Efficient Surrogate Model Assisted Estimation of Distribution algorithm for Expensive optimization

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IEEE ACCESS 2024年 12卷 78248-78260页

作者： Shang, Jin Li, Guiying Hao, Hao Zhang, Yufang Wuxi Inst Technol Inst Control Technol Wuxi 214121 Peoples R China Heilongjiang Univ Sch Mech & Elect Engn Harbin 150080 Heilongjiang Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China

In recent years, several surrogate assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. These problems lack explicit expressions and are characterized by high invocation costs. SAEAs leverage surrogate models to accelerate convergence towards the optimal region and reduce the number of function evaluations. While Gaussian Processes (GPs) are widely used due to their robustness and capability of providing uncertainty estimates, their applicability becomes limited in scenarios involving a large number of samples or high-dimensional spaces. This is due to their cubic time complexity in relation to the number of samples, which results in prohibitive computational demands for large-scale problems. To address the challenge, this work presents an efficient surrogate model-assisted estimation of the distribution algorithm (ESAEDA). This method employs a random forest as a surrogate model and combines it with a GP-hedge acquisition strategy to ensure the efficiency and accuracy of model-assisted selection. An improved EDA model called the variable-width histogram model with some unevaluated solutions is used to generate new solutions. To demonstrate the benefits of the proposed method, we compared ESAEDA with several state-of-the-art surrogate-assisted evaluation algorithms and the Bayesian optimization method. Experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms for two well-known test suites.

关键词： Computational modeling optimization Computational efficiency Closed box Time complexity Heuristic algorithms Gaussian processes Artificial intelligence Random forests optimization algorithm random forest

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Hybrid Evolutionary algorithm for the optimization of Interplanetary Trajectories

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JOURNAL OF SPACECRAFT AND ROCKETS 2009年第2期46卷 365-372页

作者： Sentinella, Matteo Rosa Casalino, Lorenzo Politecn Torino Dipartimento Energet I-10129 Turin Italy

A hybrid evolutionary algorithm is applied to the design of interplanetary trajectories with multiple impulses and gravity assists. The optimization procedure runs three different optimizers based on genetic algorithms, differential evolution, and particle swarm optimization "in parallel";the algorithms, which can also he employed separately, are used synergistically here by letting the best individuals, found by each-algorithm, migrate to the others at prescribed intervals. A comparison with the results presented In recent literature, which state that differential evolution is well suited to deal with this kind of problem, is carried out. The performance of the hybrid optimizer is comparable to that of differential evolution in terms of computational time and function evaluations when problems with a reduced number of variables are considered. The hybrid optimizers may instead exhibit better performance when more complex problems are dealt with. The results also show that the algorithm performance is remarkably improved by introducing a "mass mutation' operator to avoid premature converge nee to suboptimal solutions and by means of a particular choice of the variables to describe the trajectory.

关键词： Particle Swarm optimization Interplanetary Trajectories Saturn Genetic algorithm Cassini Mission Orbital Period Lambert's Problem optimization algorithm Deep Space Maneuver Artificial Neural Network

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Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-33页

作者： Mushthofa, Malik Thedy, John Teguh, Mochamad Purwanto Pratama, Adjie Gemilang Han, Ay Lie Department of Civil Engineering Islamic University of Indonesia Yogyakarta 55584 Indonesia Department of Bioenvironmental Systems Engineering National Taiwan University Taipei 10617 Taiwan Department of Civil Engineering Diponegoro University Semarang 50275 Indonesia

This systematic review explores the application of Artificial Intelligence (AI) in optimizing the mix design of fly ash-based geopolymer concrete (FABGC). Analyzing studies published between 2014 and 2025, it examines key methodologies, including machine learning models,a* optimization algorithms, and multi-criteria decision-making approaches. Critical aspects such as data preprocessing, AI model selection, hyperparameter tuning, explainable AI (XAI), and optimization strategies are synthesized to provide a comprehensive perspective on AI-driven FABGC research. The review identifies Deep Residual Networks (ResNet) and Extreme Gradient Boosting (XGB) as the most accurate models for predicting FABGC strength, consistently outperforming others due to their lower error metrics. Backpropagation Neural Networks (BPNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) also demonstrate competitive performance, while Random Forest (RF) and Decision Tree (DT) models excel in computational efficiency with shorter training times. Despite being the most widely implemented, Artificial Neural Networks (ANN) rarely achieve the highest predictive accuracy. Traditional regression methods, though straightforward, lag behind in performance. These findings underscore the need for standardized datasets, enhanced collaboration, and innovative AI-driven approaches to improve FABGC mix design optimization. Addressing these challenges will facilitate more reliable and efficient AI applications in sustainable concrete technology. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Artificial intelligence Fly ash Geopolymer concrete Mix design optimization algorithm

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Artificial meerkat algorithm: a new metaheuristic algorithm for solving optimization problems

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PHYSICA SCRIPTA 2024年第12期99卷 125280-125280页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

In this study, a novel artificial meerkata* optimization algorithm (AMA) is proposed to simulate the cooperative behaviors of meerkat populations. The AMA algorithm is designed with two sub-populations, multiple search strategies, a multi-stage elimination mechanism, and a combination of information sharing and greedy selection strategies. Drawing inspiration from the intra-population learning behavior, the algorithm introduces two search mechanisms: single-source learning and multi-source learning. Additionally, inspired by the sentinel behavior of meerkat populations, a search strategy is proposed that combines Gaussian and L & eacute;vy variations. Furthermore, inspired by the inter-population aggression behavior of meerkat populations, the AMA algorithm iteratively applies these four search strategies, retaining the most suitable strategy while eliminating others to enhance its applicability across complex optimization problems. Experimental results comparing the AMA algorithm with seven state-of-the-art algorithms on 53 test functions demonstrate that the AMA algorithm outperforms others on 71.7% of the test functions. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the AMA algorithm over alternative algorithms.

关键词： artificial meerkat algorithm mechanism multiple search strategies multi-stage gaussian variation optimization algorithm engineering

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