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Coupled Subset Simulation and Moving Least-Squares Method for Reliability-Based Control optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2021年第8期44卷 1550-1558页

作者： Shi, Dalong Holzapfel, Florian Tech Univ Munich Inst Flight Syst Dynam D-85748 Garching Germany

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The use of new intelligent techniques in designing retaining walls

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ENGINEERING WITH COMPUTERS 2020年第1期36卷 283-294页

作者： Koopialipoor, Mohammadreza Murlidhar, Bhatawdekar Ramesh Hedayat, Ahmadreza Armaghani, Danial Jahed Gordan, Behrouz Mohamad, Edy Tonnizam Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran Univ Teknol Malaysia Ctr Trop Geoengn GEOTROPIK Fac Civil Engn Johor Baharu 81310 Malaysia Colorado Sch Mines Fac Civil & Environm Engn Golden CO 80401 USA Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Skudai 81310 Johor Malaysia

The stability of retaining walls against overturning is analyzed in this study using artificial intelligence methods. Five input parameters including wall height, wall thickness, soil friction angle, soil density, and stone cement mixture density were varied and 2000 cases were considered in developing the predictive models. Using the artificial neural network (ANN) method, eight prediction models were developed and evaluated based on the coefficient of determination (R-2) and the root mean square error. R-2 values of 0.9740 and 0.9824 for training and testing datasets, respectively (for the best model), indicate the level of ANN capability in predicting safety factor (SF) of retaining walls. After developing the ANN model, the ant colony optimization (ACO) algorithm was used to maximize the safety factor of the wall by varying the input parameters. In fact, the best ANN model was selected to be used as a modeling function in ACO algorithm. The SF result from optimization section was obtained as 3.057 which show a significant difference from the mean SF values used in the modeling. It can be concluded that ACO may be used as a powerfula* optimization algorithm in optimizing SF results of retaining walls.

关键词： Stone masonry retaining wall Safety factor ANN ACO optimization algorithm

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Multi-area multi-source automatic generation control in deregulated power system

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ENERGY 2020年 201卷 117667-117667页

作者： Ghasemi-Marzbali, Ali Mazandaran Univ Sci & Technol Dept Elect & Biomed Engn Babol Sar Iran

The restructuring of the interconnected power systems under the Independent System Operator (ISO) managing to get an unforced and independent electricity market is the ultimate goal for the federal energy regulatory commission (FERC). Therefore, it is converted a common topic of general studies for the period of last years. Therefore, FERC presents various ancillary services such as frequency control based on load following under automatic generation control (AGC) in the deregulated environment. This paper presents a multi-area multi-source AGC in a deregulated power system that operates under deregulation platform on the bilateral policy having the thermal power plants with reheat turbine, gas, diesel and hydro power units in the form of a four-area interconnected power system with various of the possible contracts. Since damping of frequency oscillation under unexpected faults is very important to grantee system stability, therefore, the optimal tuning of proportional-integral-derivative controller with low pass filter is converted a single objective function optimization problem considering system parameter's uncertainties and eigenvalues. The important benefit of this approach is its high insensitivity to large load changes and disturbances in the existence of unit parameter discrepancy and model's nonlinearities. To cope with the disadvantage of the conventional controllers in the AGC power system, the proposed optimization problem is solved by a new modified virus colony search (MVCS). Simulations are carried out for a large-scale AGC power system under the deregulated regime with the various possible contracts and load distributions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Automatic generation control optimization algorithm Deregulated structure Modified VCS Renewable resource

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Time -frequency analysis of a new aperiodic resonance

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2020年 85卷 105258-105258页

作者： Yang, Jianhua Zhang, Shuai Sanjuan, Miguel A. F. Liu, Houguang China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Jiangsu Peoples R China Univ Rey Juan Carlos Nonlinear Dynam Chaos & Complex Syst Grp Dept Fis Tulipan S-N Madrid 28933 Spain Kaunas Univ Technol Dept Appl Informat Studentu 50-407 LT-51368 Kaunas Lithuania

Based on the time-frequency analysis, a piecewise re-scaled method is proposed to realize aperiodic resonance in a Duffing system excited by the nonlinear frequency modulated (NLFM) signal. Based on the aperiodic resonance theory, the weak NLFM signal is enhanced greatly. By short time Fourier transform, numerical simulations are carried out for several kinds of NLFM signal. The results show that the method enhances the NLFM signal effectively. Meanwhile, the effectiveness of the method is still illustrated in the noise background. In addition, the noise and the interference frequency can be removed. Noteworthy, and differently as what happens with the stochastic resonance and vibrational resonance, the aperiodic resonance does not require any auxiliary signal or noise to induce it. This constitutes also a new result of this paper. Next, in order to expand the application of this method, it is used to process the experiment signal of bearing fault under variable speed condition. The validity of the method is illustrated again. The results provide new reference in processing non-stationary frequency-modulated signal. Finally, an adaptive piecewise re-scaled aperiodic resonance scheme is put forward to get optimal parameters to induce stronger aperiodic resonance quickly.

关键词： Aperiodic resonance optimization algorithm Nonlinear frequency modulated signal Signal enhancement

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A multi-strategy fusion artificial bee colony algorithm with small population

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 142卷 112921-000页

作者： Song, Xiaoyu Zhao, Ming Xing, Shuangyun Shenyang Jianzhu Univ Informat & Control Engn Fac Shenyang 110168 Liaoning Peoples R China Shenyang Jianzhu Univ Sch Sci Shenyang 110168 Liaoning Peoples R China

Although artificial bee colony (ABC) algorithm is more and more popular in solving complex problems, slow convergence rate limits its wide application. ABC with small population can use the limited function evaluation times more efficiently since it can avoid unnecessary searches. However, ABC with small population cannot ensure population diversity, and when the algorithm is weak or unstable, it may fall into local optimum easily. So based on the latest research, we are motivated to propose a stabler and more efficient algorithm design to improve the search ability of ABC with small population by the fusion of multiple search strategies, which used together for the employed bees and the onlooker bees. Firstly we select and design multiple strategies with different search abilities of exploration and exploitation. Secondly, we propose an evolution ratio, which is an indicator to fully reflect the adaptability of the search strategy. Thirdly, we design different fusion methods according to the characteristics of the strategies, in which the search strategy with high exploration is maintained at a certain frequency throughout the whole search process of the employed bees, and the selections of the other two search strategies are adjusted according to evolution ratio adaptively in the employed bee phase and the onlooker bee phase. In the end, a novel algorithm called MFABC is proposed, which can realize efficiently multi-strategy cooperative search according to the requirements of different problems and different search stages. Experimental results on a set of benchmark functions have shown the accuracy, stability, efficiency and convergence rate of MFABC. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： optimization algorithm Artificial bee colony algorithm Multi-strategy fusion Small population Cooperative searching

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Mode-Merging Design Method for Nonlocally Reacting Liners with Porous Materials

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AIAA JOURNAL 2020年第6期58卷 2533-2545页

作者： Qiu, Xianghai Jing, Xiaodong Du, Lin Sun, Xiaofeng Abom, Mats Boden, Hans Beihang Univ Sch Energy & Power Engn Fluid & Acoust Engn Lab Beijing 100191 Peoples R China Royal Inst Technol Aeronaut & Vehicle Engn Marcus Wallenberg Lab Sound & Vibrat Res SE-10044 Stockholm Sweden

A mode-merging design method (MMDM) for bulk liners with porous materials is proposed to maximize transmission losses in flow ducts. The eigenequation and its partial derivative equation governing the coupled acoustic fields in the duct and backchamber are derived, from which the merging double eigenvalue is obtained at a target frequency for a single incident circumferential mode. The crucial liner parameters (namely. the chamber depth and the flow resistivity of the porous material) can be optimized simultaneously. A finite element propagation model is employed to evaluate the MMDM-based design for a finite-length liner, demonstrating the effectiveness of the method for selected typical design cases. It is found that mode merging can be realized for a nonlocally reacting liner, with or without a perforated plate, mainly for protecting the porous material inside its chamber. The MMDM, under the assumption of an infinite-length liner, can give an almost globally optimal design for a finite-length liner when the length exceeds five times the duct diameter. It is expected that the proposed MMDM can be useful in the preliminary designs for actual bulk liners due to its rapidness and accuracy.

关键词： Porous Materials Acoustic Impedance Bessel Functions Galerkin Finite Element Method Flow Conditions Mach Number optimization algorithm Auxiliary Power Unit Kinematic Viscosity Cylindrical Coordinate System

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A Spark-Based Artificial Bee Colony algorithm for Unbalanced Large Data Classification

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INFORMATION 2022年第11期13卷 530页

作者： Al-Sawwa, Jamil Almseidin, Mohammad Tafila Tech Univ Comp Sci Dept Tafila 66110 Jordan

With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F-1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.

关键词： big data analytic artificial bee colony data classification Apache Spark optimization algorithm swarm intelligence

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A novel atom search optimization for dispersion coefficient estimation in groundwater

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 91卷 601-610页

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

A new type of meta-heuristic global optimization methodology based on atom dynamics is introduced. The proposed Atom Search optimization (ASO) approach is a population-based iterative heuristic globala* optimization algorithm for dealing with a diverse set of optimization problems. ASO mathematically models and mimics the atomic motion model in nature, where atoms interact with each other through interaction forces resulting form Lennard-Jones potential and constraint forces resulting from bond length potential, the algorithm is simple and easy to implement. ASO is applied to a dispersion coefficient estimation problem, the experimental results demonstrate that ASO can outperform other well-known approaches such as Particle Swarm optimization (PSO), Genetic algorithm (GA) and Bacterial Foraging optimization (BFO) and that ASO is competitive to its competitors for parameter estimation problems. The source codes of ASO are available at https://***/matlabcentral/fileexchange/67011-atom-search-optimization-aso-algorithm?s_tid=srchtitle. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Heuristic algorithm optimization algorithm Dispersion coefficient Parameter estimation Global optimization Metaheuristic Atom search optimization

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Aerostructural Design optimization Using a Multifidelity Quasi-Newton Method

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JOURNAL OF AIRCRAFT 2019年第5期56卷 2019-2031页

作者： Bryson, Dean E. Rumpfkeil, Markus P. Aerosp Syst Directorate Wright Patterson AFB OH 45433 USA Univ Dayton Dept Mech & Aerosp Engn Dayton OH 45469 USA Air Force Res Lab Design & Anal Branch Aerosp Vehicles Div Wright Patterson AFB OH 45433 USA

The traditional aircraft design process relies upon low-fidelity models for expedience and resource savings. However, the reduced accuracy and reliability of low-fidelity tools often lead to the discovery of design defects or inadequacies late in the design process. These deficiencies result in either costly changes or the acceptance of a configuration that does not meet expectations. Multifidelity methods attempt to blend the increased accuracy and reliability of high-fidelity models with the reduced cost of low-fidelity models. A new multifidelity algorithm has been proposed, combining elements from typical trust region model management and classical quasi-Newton methods. In this paper, the algorithm is compared with a single-fidelity quasi-Newton method for complex aeroelastic simulations. The vehicle design problem includes variables for planform shape, structural sizing, and cruise condition with constraints on trim and structural stresses. Considering the objective function reduction versus computational expenditure, the multifidelity process performs better in three of four cases in early iterations compared with a single-fidelity approach. A contracting trust region is found to slow the progress of the multifidelity optimizer. However, by leveraging the approximate inverse Hessian, the optimization can be seamlessly continued using high-fidelity data alone. Ultimately, the proposed new algorithm produces better designs in the cases considered. Investigating the return on investment confirms that the multifidelity advantage is greatest in early iterations, and managing the transition to pure high-fidelity optimization is critical.

关键词： Newton's Method Aircraft Design Process Stability Derivatives Aerodynamic Performance Aspect Ratio Structural Analysis FUN3D optimization algorithm Finite Element Modeling Lift to Drag Ratio

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Aeroelastic Sizing and Layout Design of a Wingbox Through Nested optimization

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AIAA JOURNAL 2019年第2期57卷 848-857页

作者： Stanford, Bret K. Jutte, Christine V. Coker, Christian A. NASA Langley Res Ctr Aeroelast Branch Hampton VA 23681 USA Craig Technol Inc Adv Mat & Proc Branch Cape Canaveral FL 32920 USA Mississippi State Univ Dept Aerosp Engn Starkville MS 39759 USA

The goals of this work are to 1) develop ana* optimization algorithm that can simultaneously handle a large number of sizing variables and topological layout variables for an aeroelastic wingbox optimization problem and 2) use this algorithm to ascertain the benefits of curvilinear wingbox components. The algorithm used here is a nested optimization, in which the outer level optimizes the rib and skin stiffener layouts with a surrogate-based optimizer, and the inner level sizes all of the components via gradient-based optimization. Two optimizations are performed: one restricted to straight rib and stiffener components only, and the other allowing curved members. A moderate 1.18% structural mass reduction is obtained through the use of curvilinear members. A global sensitivity analysis of the topology variables, with and without the effect of the nested sizing variables, is also presented.

关键词： Wingbox Sensitivity Analysis optimization algorithm Surrogate Model Finite Element Analysis High Aspect Ratio Topology optimization Sweep Angle Ailerons Stress Concentration

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