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Numerical Simulation Study on optimization of Freezing Pipe Position under Seepage Conditions Based on Simulated Annealing algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第12期28卷 5441-5451页

作者： Song, Renjie Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China

Artificial Ground Freezing (AGF) is a promising method for controlling seepage in permeable strata. However, AGF faces challenges, including difficulties in achieving a frozen barrier in high-flow conditions and concerns about cost-effectiveness. This study optimizes freezing pipe placement in AGF using a simulated annealing algorithm and a coupled hydrothermal finite element model, focusing on AGF system responses under varying seepage velocities. The optimized layout significantly reduces freeze-ring formation time (by 2.5 days) and the overall freezing duration (by 12.5 days). Moreover, it substantially decreases the required frozen soil volume, facilitating drilling and excavation. Across different seepage velocities, the difference in freeze-ring formation time between the optimized and uniform layouts gradually increases with higher seepage velocity, reaching a maximum difference of 5.9 days. Finally, the relationship between freezing time and seepage velocity was quantitatively described using exponential functions. This study underscores the critical role of optimizing freezing pipe placement in AGF, providing a foundation for efficient and cost-effective geotechnical engineering practices.

关键词： Hydrothermal coupling Artificial ground freezing Placement of freezing pipes optimization algorithm Seepage

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Bandgap and wave attenuation optimization of tetra-chiral metamaterial using PSO algorithm

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JOURNAL OF VIBRATION AND CONTROL 2024年第11-12期30卷 2402-2413页

作者： Tikani, Vahid Ziaei-Rad, Saeed Moosavi, Hassan Isfahan Univ Technol Dept Mech Engn Esfahan *** Iran

The development of vibration suppression systems with desired efficiency and low cost is one of the significant challenges in engineering. In this study, a parametric lattice model is considered to analyze the wave mitigation features in the metamaterial based on a tetra-chiral topology of the periodic cell equipped with internal resonators. Bloch wave theorem and finite element method are employed to explore the bandgap of the structure and its wave mitigation features. Since the unit cell geometry can be designed to open and shift bandgaps, particle swarm optimization algorithm is used to find the largest possible gap in the desired frequency range. The optimization method is programmed using MATLAB combined with an in-house finite element solver, considering the parameters' ranges to ensure geometric compatibility. In all studied cases, the optimized geometry leads to superior vibration suppression and larger complete bandgaps.

关键词： mechanical metamaterial vibration attenuation optimization algorithm bandgap wave mitigation

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Wind farm layout optimization based on dynamic Levy sparrow search algorithm: A multi-parameter analysis with active yaw control

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ENERGY 2025年 324卷

作者： Shen, Lian Zhou, Pinhan Han, Yan Mi, Lihua Xu, Wenxiang Xu, Jialu Changsha Univ Sch Civil Engn Changsha 410022 Peoples R China Changsha Univ Sci & Technol Hunan Prov Res Ctr Safety Control Technol & Equipm Changsha 410004 Peoples R China Wenzhou Univ Technol Coll Architecture & Energy Engn Wenzhou 325035 Peoples R China

Wind farm layout optimization is essential for improving power generation efficiency and reducing operational costs in wind farms. This study develops a multi-turbine wake superposition model incorporating turbine yaw effects, based on a three-dimensional polynomial wake model. Dynamic weight and Levy flight strategies are employed to enhance the sparrow search algorithm (SSA) for layout optimization. The proposed wake model is validated with experimental data, and the superiority of DLSSA is confirmed through comparisons with traditional algorithms. Parameter analysis of active yaw strategy is conducted using two tandem wind turbines. Integrating DLSSA with the wake model, layout optimization considering height variation and active yawing strategies is investigated using dimensionless annual energy production (DAEP) as the objective function. Simulation data suggests that optimal total power output is attained when two wind turbines are positioned in a tandem configuration, with the upstream turbine set at a yaw angle of 15 degrees. Incorporating height variation and active yaw control significantly enhances the total power output of wind farms. Implementing these strategies in layout optimization can increase total power output by 1.32 %-10.86 % compared to alternative layouts. Notably, joint optimization surpasses sequential optimization, resulting in a 1.70 % higher total power output.

关键词： Three-dimensional wake model Layout optimization Active yawing control optimization algorithm

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The Bedbug Meta-heuristic algorithm to Solve optimization Problems

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Journal of Bionic Engineering 2023年第5期20卷 2465-2485页

作者： Kouroush Rezvani Ali Gaffari Mohammad Reza Ebrahimi Dishabi Department of Computer Engineering Miyaneh BranchIslamic Azad UniversityMiyanehIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran

Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.

关键词： Bedbug Meta-Heuristic algorithm optimization algorithm BMHA

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A Unit Root Test with Markov Switching Deterministic Components: A Special Emphasis on Nonlinear optimization algorithms

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COMPUTATIONAL ECONOMICS 2024年第3期64卷 1837-1856页

作者： Omay, Tolga Corakci, Aysegul Atilim Univ Dept Econ TR-06830 Ankara Turkiye Cankaya Univ Dept Econ TR-06790 Ankara Turkiye

In this study, we investigate the performance of different optimization algorithms in estimating the Markov switching (MS) deterministic components of the traditional ADF test. For this purpose, we consider Broyden, Fletcher, Goldfarb, and Shanno (BFGS), Berndt, Hall, Hall, Hausman (BHHH), Simplex, Genetic, and Expectation-Maximization (EM) algorithms. The simulation studies show that the Simplex method has significant advantages over the other commonly used hill-climbing methods and EM. It gives unbiased estimates of the MS deterministic components of the ADF unit root test and delivers good size and power properties. When Hamilton's (Econometrica 57:357-384, 1989) MS model is re-evaluated in conjunction with the alternative algorithms, we furthermore show that Simplex converges to the global optima in stationary MS models with remarkably high precision and even when convergence criterion is raised, or initial values are altered. These advantages of the Simplex routine in MS models allow us to contribute to the current literature. First, we produce the exact critical values of the generalized ADF unit root test with MS breaks in trends. Second, we derive the asymptotic distribution of this test and provide its invariance feature.

关键词： Markov switching model Unit root optimization algorithm C12 C22 C24

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An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

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DISCOVER APPLIED SCIENCES 2024年第8期6卷 437页

作者： Bhat, Soumya J. Venkata, Santhosh Krishnan VTU Dept Comp Sci & Engn Shri Madhwa Vadiraja Inst Technol & Management Bantakal 574115 Karnataka India Manipal Inst Technol Manipal Acad Higher Educ Ctr Cyber Phys Syst Dept Instrumentat & Control Engn Manipal 576104 Karnataka India

Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm optimization to determine the nodes' locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions. A unique approach to minimize the impact of obstructions in estimating the locations of a randomly formed WSN. A novel method for node localization using a newly developed optimization algorithm called VPPSO. The applicability of the algorithm for detecting oil and gas reservoirs has been tested.

关键词： DVHop Localization Oil and gas reservoirs optimization algorithm Wireless sensor networks

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Efficient Correlation Method for Satellite Thermal Analysis Model Using Multiple Linear Regression and optimization algorithms

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2023年第5期24卷 1257-1270页

作者： Kang, Jaewon Kim, Keon Woong Shin, Somin Kim, Jeong Ho Inha Univ Dept Aerosp Engn 36 Gaetbeol Ro Incheon 22212 South Korea Agcy Def Dev POB 35 Daejeon 34186 South Korea

As the thermal analysis model of satellites is used as an important indicator for thermal design, it must accurately simulate the thermal behaviour of actual satellites for precise thermal design. To increase the accuracy of the thermal analysis model, it must be correlated using the thermal balance test data for actual satellite models. To achieve this, we herein propose an efficient correlation method for satellite thermal analysis models using multiple linear regression techniques with quadratic terms and optimization algorithms. The proposed method reduces the amount of computation by choosing dominant parameters through sensitivity analysis and creating a multiple linear regression model that can replace the thermal analysis model in the subsequent optimization process. Subsequently, optimization algorithms are applied to the multiple linear regression model to perform the correlation of the thermal analysis model. In this study, the numerical validation of the proposed method was performed using numerical data from a reference thermal analysis model to verify the reliability and accuracy of the proposed method before it was applied to the correlation of the thermal analysis model using experimental data. The thermal analysis result of the reference thermal analysis model was set as the target value to correlate, and quantitative performance evaluation was performed for various combinations of optimization algorithms and design of experiments methods by comparing the estimated analysis parameters. The results of this study demonstrate that the proposed method can efficiently produce an accurate correlation model for thermal analysis.

关键词： Satellite structural thermal model Thermal analysis model Multiple linear regression optimization algorithm Correlation method

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Application of Ant Colony optimization Improved Clustering algorithm in Malicious Software Identification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第1期15卷 1031-1039页

作者： Qian, Yong Sichuan Coll Architectural Technol Int Sch Tech Educ Deyang 618000 Peoples R China

Due to the increasing threat of malware to computer systems and networks, traditional malware detection and recognition technologies face difficulties and limitations. Therefore, exploring new methods to improve the accuracy and efficiency of malware identification has become an urgent need. This study introduces ant colony algorithm to optimize traditional clustering algorithms and algorithm parameters. The experimental results showed that the improvement rates of the improved algorithm in accuracy, echo value, and false alarm rate were 0.253, 0.115, and 0.056, respectively. The accuracy on the training and validation sets continued to increase and the loss curve continued to decrease. In addition, the improved algorithm had stronger modeling ability for data feature relationships and temporal information. This is of great help in improving the recognition ability of virus and worm software. The improved algorithm had a lower occupancy rate of computing resources compared to other algorithms, but it could also effectively monitor device operation. Compared with traditional methods, this method can more accurately identify malicious software and effectively identify malicious software samples from large-scale datasets. This is of great significance for protecting computer systems and network security.

关键词： Ant colony algorithm clustering algorithm malicious software identification computer security optimization algorithm

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Metamodel-Based Hyperparameter optimization of optimization algorithms in Building Energy optimization

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BUILDINGS 2023年第1期13卷 167页

作者： Si, Binghui Liu, Feng Li, Yanxia Nanjing Tech Univ Coll Architecture Nanjing 210044 Peoples R China Southeast Univ Sch Architecture Nanjing 210096 Peoples R China

Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters. Currently, studies focusing on algorithm hyperparameters are scarce, and common agreement on how to set their values, especially for BEO problems, is still lacking. This study proposes a metamodel-based methodology for hyperparameter optimization of optimization algorithms applied in BEO. The aim is to maximize the algorithmic efficacy and avoid the failure of the BEO technique because of improper algorithm hyperparameter settings. The method consists of three consecutive steps: constructing the specific BEO problem, developing an ANN-trained metamodel of the problem, and optimizing algorithm hyperparameters with nondominated sorting genetic algorithm II (NSGA-II). To verify the validity, 15 benchmark BEO problems with different properties, i.e., five building models and three design variable categories, were constructed for numerical experiments. For each problem, the hyperparameters of four commonly used algorithms, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, simulated annealing (SA), and the multi-objective genetic algorithm (MOGA), were optimized. Results demonstrated that the MOGA benefited the most from hyperparameter optimization in terms of the quality of the obtained optimum, while PSO benefited the most in terms of the computing time.

关键词： building energy efficiency building energy optimization hyperparameter optimization optimization algorithm ANN-trained metamodel

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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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