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Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第9期30卷 22396-22412页

作者： Zhao, Long Zhao, Xinbo Li, Yuanze Shi, Yi Zhou, Hanmi Li, Xiuzhen Wang, Xiaodong Xing, Xuguang Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471000 Henan Peoples R China Henan Univ Sci & Technol Coll Agr Luoyang 471000 Henan Peoples R China Northwest A&F Univ Minist Educ Key Lab Agr Soil & Water Engn Arid Area Yangling 712100 Shaanxi Peoples R China

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources;this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionica* optimization algorithms (i.e., sparrow searcha* optimization algorithm, Harris Hawksa* optimization algorithm, and lion swarma* optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO;the hybrid model optimized using the bionica* optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R-2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

关键词： Reference crop evapotranspiration KELM algorithm optimization algorithm Factor importance Modeling Arid and semiarid regions

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Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第2期10卷 1-12页

作者： Mohebian, Pooya Motahari, Mohammad Reza Aval, Seyed Bahram Beheshti Arak Univ Fac Engn Dept Civil Engn Arak Iran KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Bridges are one of the most pivotal civil engineering structures. Nevertheless, they are highly susceptible to localized damage, which poses a gradual threat to their long-term stability and functionality. Hence, it is of utmost importance to detect any damage in bridges to ensure their continued safe and reliable performance. In view of this, the present study proposes an optimization-based finite element model updating method to perform arch bridge damage detection by using vibration data and the gradient-based optimizer (GBO) algorithm. The damage severity of structural elements in different parts of the bridge, including those in the arch, columns, and deck, are taken as damage variables. The objective function of the optimization problem is also defined in terms of discrepancies between the vibration data of the actual damaged bridge and those computed from the finite element model. The GBO algorithm, as a new robust meta-heuristic algorithm, is applied to tackle the damage detection problem, and its performance is compared to other well-known meta-heuristic algorithms. The applicability of the proposed method is numerically assessed on an arch bridge structure under both noise-free and noisy conditions. The obtained results demonstrate the accuracy and computational efficiency of the presented method in dealing with the arch bridge damage detection problem.

关键词： Damage detection Arch bridge Vibration method Finite element model updating optimization algorithm

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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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An image encryption scheme based on an optimal chaotic map derived by multi-objective optimization using ABC algorithm

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NONLINEAR DYNAMICS 2021年第2期105卷 1885-1909页

作者： Toktas, Abdurrahim Erkan, Ugur Ustun, Deniz Karamanoglu Mehmetbey Univ Fac Engn Dept Elect & Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Fac Engn Dept Comp Engn TR-70200 Karaman Turkey Tarsus Univ Fac Engn Dept Comp Engn TR-33400 Tarsus Mersin Turkey

A novel optimal chaotic map (OCM) is proposed for image encryption scheme (IES). The OCM is constructed using a multi-objective optimization strategy through artificial bee colony (ABC) algorithm. An empirical model for the OCM with four unknown variables is first constituted, and then, these variables are optimally found out using ABC for minimizing the multi-objective function composed of the information entropy and Lyapunov exponent (LE) of the OCM. The OCM shows better chaotic attributes in the evaluation analyses using metrics such as bifurcation, 3D phase space, LE, permutation entropy (PE) and sample entropy (SE). The encrypting performance of the OCM is demonstrated on a straightforward IES and verified by various cryptanalyses that compared with many reported studies, as well. The main superiority of the OCM over the studies based on optimization is that it does not require any optimization in the encrypting operation;thus, OCM works standalone in the encryption. However, those reported studies use ciphertext images obtained through encrypting process in every cycle ofa* optimization algorithm, resulting in long processing time. Therefore, the IES with OCS is faster than the others optimization-based IES. Furthermore, the proposed IES with the OCM manifests satisfactory outcomes for the compared results with the literature.

关键词： Image encryption Chaotic map optimization algorithm Multi-objective optimization Artificial bee colony

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An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems

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ENGINEERING COMPUTATIONS 2021年第5期38卷 2228-2251页

作者： Yuan, Yongliang Wang, Shuo Lv, Liye Song, Xueguan Henan Polytech Univ Sch Mech & Power Engn Jiaozuo Henan Peoples R China Dalian Univ Technol Sch Mech Engn Dalian Peoples R China Zhejiang Sci Tech Univ Fac Mech Engn & Automat Hangzhou Peoples R China

Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improveda* optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014's special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effectivea* optimization algorithm for solving constrained optimization problems.

关键词： Adaptive resistance and stamina strategy Bucket wheel reclaimer Dragonfly algorithm optimization algorithm

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optimization of thin-wall structures using hybrid gravitational search and Nelder-Mead algorithm

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MATERIALS TESTING 2016年第1期58卷 75-78页

作者： Yildiz, Ali Riza Kurtulus, Enes Demirci, Emre Yildiz, Betul Sultan Karagoz, Selcuk Bursa Tech Univ Dept Mech Engn Bursa Turkey Bursa Tech Univ Nat Sci & Engn Fac Bursa Turkey Bursa Tech Univ MPDOL Bursa Turkey Univ Michigan Ann Arbor MI 48109 USA Mississippi State Univ CAVS Mississippi State MS 39762 USA Yesilova Holding Istanbul Turkey Bursa Tech Univ Bursa Turkey

In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrida* optimization algorithm based on gravitational search algorithm and Nelder-Mead algorithm is introduced to improve crash performance of vehicles during frontal impact. The results show that the hybrid approach is very effective to develop crash performance of the vehicle components and thin-wall structures.

关键词： Thin-wall structures crashworthiness automotive optimization algorithm

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Swarm algorithm for single- and multiobjective airfoil design optimization

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AIAA JOURNAL 2004年第2期42卷 366-373页

作者： Ray, T Tsai, HM Natl Univ Singapore Temasek Labs Singapore 119260 Singapore

Shape optimization of airfoils involves highly expensive, nonlinear objective(s) and constraint functions often with functional and slope discontinuity that limits the efficient use of gradient-based methods for its solution. Gradient-based methods are not capable of generating a set of pareto solutions as required in multiobjective problems as they work with a single solution and improve it through successive iterations. Population-based, zero-order, stochastic optimization methods are therefore an attractive choice for shape optimization problems as they are easy to implement and effective for highly nonlinear problems. We present a swarm algorithm that is applicable for optimization problems in general, but is here explored for airfoil design optimization studies. The algorithm is based on a sociobehavioral model, and it offers the designer the desired flexibility to solve various unconstrained/constrained, single-/multiobjective forms of the airfoil shape optimization problem. The algorithm handles objectives and constraints separately via pareto ranking and is thus immune to problems of scaling and aggregation that commonly affect penalty-function-based constraint handling schemes. Three different airfoil design optimization problems have been solved to illustrate the algorithm's flexibility and its computational efficiency, which compare favorably with existing stochastic search methods.

关键词： Evolutionary algorithm RAE 2822 Shape optimization Stochastic optimization Aerodynamic Performance Drag Coefficient Computational Fluid Dynamics optimization algorithm Genetic algorithm Artificial Neural Network

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Holonic-C2 organization Structure Generation Method Based on Clusteringa* optimization algorithms

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IEEE ACCESS 2019年 7卷 118459-118471页

作者： Wang, Xun Zhang, Jieyong Wan, Lujun Jiao, Zhiqiang Air Force Engn Univ Informat & Nav Coll Xian 710077 Shaanxi Peoples R China Air Force Engn Univ Air Traff Control & Nav Coll Xian 710051 Shaanxi Peoples R China

For Holonic-C2 organization-structure generation, a set of methods based on task-cluster clustering and platform-set optimization are proposed. Initially, the mathematical descriptions of the Holonic-C2 organization components are presented, and the characteristics and advantages of the Holonic-C2 organization are analyzed. To address the critical problems of task cluster and platform-set construction in the organization structure, with the equalization of the task resource requirements and platform resource capabilities, the mathematical models for two-stage clustering optimization are established, respectively. The multi-view clustering optimization and neighborhood search artificial bee colony algorithms are proposed and applied, respectively, for solving these mathematical models. Finally, Monte Carlo simulations are performed, and the obtained results demonstrate the effectiveness of the proposed models and algorithms.

关键词： Artificial bee colony algorithm holonic-C2 organization K-means multi-view clustering neighborhood search optimization algorithm resource balance

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Euler/Navier-Stokes optimization of supersonic wing design eased on evolutionary algorithm

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AIAA JOURNAL 1999年第10期37卷 1327-1328页

作者： Oyama, A Obayashi, S Nakahashi, K Nakamura, T Tohoku Univ Dept Aeronaut & Space Engn Nakahashi Lab Sendai Miyagi 9808579 Japan Natl Aerosp Lab Dept Aeronaut & Space Engn Tokyo 1828522 Japan

An evolutionary algorithm (EA) coupled with a Euler/Navier-Stokes code has been applied to supersonic wing shape design. Aerodynamic evaluations are distributed to the processing elements (PE) of the numerical wind tunnel (NWT) to overcome the enormous computational time necessary for the optimization. The design yields both the minimum drag and the minimum volume wave drag in the given design space. The important features of supersonic wing design as compared with conventional transonic wing design are presented.

关键词： Evolutionary algorithm Aerodynamic Design Aerodynamic Performance Wing Root National Aerospace Laboratories optimization algorithm Aeroplane Camber Numerical optimization Spline Interpolation

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An Evaluation ofa* optimization algorithms for the Optimal Selection of GNSS Satellite Subsets

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REMOTE SENSING 2024年第10期16卷 1794-1794页

作者： Alluhaybi, Abdulaziz Psimoulis, Panos Remenyte-Prescott, Rasa Univ Nottingham Nottingham Geospatial Inst Nottingham NG7 2TU England Taibah Univ Dept Civil Engn Madinah 42353 Saudi Arabia Univ Nottingham Dept Civil Engn Nottingham NG7 2RD England

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites' redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS's performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS's positioning performance. Various methods, includinga* optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, fivea* optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony optimization (ACO), Genetic algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA). The assessment of thea* optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites' signal. Thea* optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investiga

关键词： GNSS GPS satellite selection WGDOP CNR-WGDOP optimization algorithm ABC ACO GA PSO SA

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