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Designing thermal demultiplexer: Splitting phonons by negative mass and genetic algorithm optimization

Chinese Physics B 2021年第3期30卷 36-40页

作者： Yu-Tao Tan Lu-Qin Wang Zi Wang Jiebin Peng Jie Ren Center for Phononics and Thermal Energy Science China-EU Joint Laboratory on NanophononicsShanghai Key Laboratory of Special Artificial Microstructure Materials and TechnologySchool of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

We propose the concept of thermal demultiplexer, which can split the heat flux in different frequency ranges intodifferent directions. We demonstrate this device concept in a honeycomb lattice with dangling atoms. From the view ofeffective negative mass, we give a qualitative explanation of how the dangling atoms change the original transport *** first design a two-mass configuration thermal demultiplexer, and find that the heat flux can flow into different ports incorresponding frequency ranges roughly. Then, to improve the performance, we choose the suitable masses of danglingatoms and optimize the four-mass configuration with genetic algorithm. Finally, we give out the optimal configuration witha remarkable effect. Our study finds a way to selectively split spectrum-resolved heat to different ports as phonon splitter,which would provide a new means to manipulate phonons and heat, and to guide the design of phononic thermal devices inthe future.

关键词： phonon transport thermal demultiplexer optimization algorithm

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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's optimization algorithm (AVOA), and the Capuchin Search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_optimization algorithm \times 1.25\lambda_optimization algorithm \times 0.02\lambda_optimization algorithm\) 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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A novel hybrid algorithm based on Biogeography-Based optimization and Grey Wolf Optimizer

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APPLIED SOFT COMPUTING 2018年 67卷 197-214页

作者： Zhang, Xinming Kang, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Henan Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Henan Peoples R China

In order to obtain a Biogeography-Based optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, BBO and GWO are improved respectively. For BBO, the mutation operator is got rid of and a differential mutation operation is merged into the migration operator to enhance the global search ability. The original migration operation is replaced by a multi-migration operation to enhance the local search ability. For GWO, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima to some degree. Then, the improved BBO and the opposition learning based GWO are hybridized by a new strategy, named single-dimensional and all-dimensional alternating strategy, to formulate HBBOG. HBBOG can effectively maximize the two algorithms' advantages and overall balance exploration and exploitation, therefore, it can obtain strong universal applicability. We make a large number of experiments on a set of various kinds of benchmark functions and CEC2014 test set and apply HBBOG to clustering optimization. The experimental results show that HBBOG outperforms quite a few state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Evolutionary algorithm Biogeography-Based optimization Grey Wolf Optimizer Opposition-based learning approach

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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 swarm 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 swarm 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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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-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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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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Cheetah optimization for optimal power management in standalone solar PV systems with EV integration

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ENGINEERING RESEARCH EXPRESS 2025年第2期7卷 025319-025319页

作者： Maurya, Archana Pandey, A. K. MMMUT Dept Elect Engn Gorakhpur India

This research investigates the application of a novel Cheetah optimization (CO) algorithm for optimal load allocation in a standalone solar photovoltaic (PV) system integrated with an electric vehicle (EV) battery. Unlike conventional MPPT techniques like Perturb & Observe, Grey Wolf optimization (GWO), and Flying Squirrel optimization (FSO), the CO algorithm, inspired by the hunting strategies of cheetahs, effectively balances exploration and exploitation of the solution space, leading to improved convergence and optimal load allocation. Simulation studies, validated by hardware-in-the-loop (HIL) testing using Opal-RT, demonstrated the superior performance of CO. CO achieved a 15% higher peak PV power (reaching 1085 W) compared to the best-performing alternative, with a tracking efficiency exceeding 99.997%. Load power delivery was enhanced by 20%, while the CO algorithm optimized battery charging with a 5% faster charging rate and a higher peak State-of-Charge (SOC). Furthermore, CO exhibited improved system stability with a voltage deflection of only 0.65% and allowable battery voltage deviation maintained within 2%. The system demonstrated low Total Harmonic Distortion (THD): 2.12% for PV power, 3.2% for load power, and 1.96% for battery power, significantly lower than other methods. Statistical analysis, including comparisons of mean values and standard deviations, further confirmed the superiority of CO in terms of energy utilization and system stability. These findings highlight the potential of the CO algorithm as a highly effective and promising solution for optimizing load allocation in standalone solar PV systems with EV battery integration, enabling improved energy utilization, reduced reliance on grid power, and enhanced system reliability.

关键词： cheetah optimization MPPT standalone PV EV battery load allocation optimization algorithm energy management

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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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An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

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JOURNAL OF COMBINATORIAL optimization 2022年第1期44卷 794-849页

作者： Falahiazar, Alireza Sharifi, Arash Seydi, Vahid Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

关键词： Dynamic multi-objective optimization problems Evolutionary algorithm Heating optimization optimization algorithm Spread-based

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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-precision 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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