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AMBO:All Members-Based Optimizer for Solving optimization Problems

Computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate ***a* optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a newa* optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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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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Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization

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STEEL AND COMPOSITE STRUCTURES 2022年第6期42卷 733-745页

作者： Li, Ning Asteris, Panagiotis G. Tran, Trung-Tin Pradhan, Biswajeet Nguyen, Hoang Wuhan Univ Technol Sch Resource & Environm Engn Wuhan 430070 Hubei Peoples R China Sch Pedag & Technol Educ Computat Mech Lab Athens 14121 Greece FPT Univ Dept Informat Technol Danang 550000 Vietnam Univ Technol Sydney Fac Engn & IT Sch Civil & Environm Engn Ctr Adv Modelling & Geospatial Informat Syst CAMG Sydney NSW 2007 Australia King Abdulaziz Univ Ctr Excellence Climate Change Res POB 80234 Jeddah 21589 Saudi Arabia Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia Hanoi Univ Min & Geol 18 Vien St Hanoi 100000 Vietnam Hanoi Univ Min & Geol Innovat Sustainable & Responsible Min ISRM Res Gr 18 Vien StDuc Thang Ward Hanoi 100000 Vietnam

This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

关键词： artificial neural network deflection of RC beam ICA-ANN modelling optimization algorithm

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Combining Shaping and Flow Control for Aerodynamic optimization

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AIAA JOURNAL 2015年第4期53卷 888-901页

作者： Zhang, M. He, L. Univ Oxford Dept Engn Sci Parks Rd Oxford OX1 3PJ England Univ Oxford Dept Engn Sci Computat Aerothermal Engn Oxford OX1 3PJ England

Shape optimization and flow control have been extensively studied in the past to improve the performance of wings and blades. However, they are commonly applied as two separate disciplines, with their interactions being rarely studied. The present work aims to examine and identify the potential to explore the interactions between the two disciplines and is chiefly concerned about two related issues: 1) how much more performance improvement can be obtained by combining the shape optimization with the flow control optimization, and 2) the effects of the sequencing of the disciplines when the optimizations are combined. To address these issues, five optimization approaches have been studied, representing the shaping only, the flow control optimization only, and three different sequencings of the disciplines in the combined optimizations. All the optimization approaches are applied consistently by using the same optimization system developed in this research. The system uses the Kriging surrogate method as thea* optimization algorithm, and it incorporates a standard computational fluid dynamics solver for which the validity and numerical sensitivities have been assessed. The developed methodology is applied to two optimization cases of practical interest: a compressor blade and a circulation controlled airfoil. The results of both cases show that, when shaping and flow control optimizations are combined, the performance enhancement is considerably higher than when the two disciplines are applied separately. Furthermore, in the combined optimizations for the airfoil, it is observed that the concurrent optimization leads to the highest performance, whereas the sequential optimizations tend to be stuck at less optimal solutions.

关键词： Circulation Controlled Airfoil Shape optimization Control optimization Design of Compressor CFD optimization algorithm Flow Conditions Aerodynamic Efficiency Freestream Mach Number Reynolds Averaged Navier Stokes

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Robust Airfoil optimization and the Importance of Appropriately Representing Uncertainty

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AIAA JOURNAL 2017年第11期55卷 3925-3939页

作者： Cook, Laurence W. Jarrett, Jerome P. Univ Cambridge Dept Engn Trumpington St Cambridge CB2 1PZ England

The importance of designing airfoils to be robust with respect to uncertainties in operating conditions is well recognized. However, often, the probability distribution of such uncertainties does not exist or is unknown, and a designer looking to perform a robust optimization is tasked with deciding how to represent these uncertainties within the optimization framework. This paper asks the following: How important is the choice of how to represent input uncertainties mathematically in robust airfoil optimization? It specifically compares probabilistically based aleatory uncertainties and interval-based epistemic uncertainties. This is first investigated by considering optimizations on several algebraic test problems, which illustrate the mechanisms by which the representation of uncertainty becomes significant in a robust optimization. This insight is then used to predict and subsequently demonstrate that, for two airfoil design problems, the advantage of doing a robust optimization over a deterministic optimization is similar, regardless of how the input uncertainties are represented mathematically. The benefit of this is potentially eliminating the time required to establish an accurate representation of the uncertainties from the preliminary stage of design, where time is a valuable resource.

关键词： NACA 0012 Probability Density Functions Aerodynamic Shape optimization Lift to Drag Ratio Lift Coefficient CFD Flow Conditions Shape optimization Robust Design optimization algorithm

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Specific Error Mode Correction for Deformable Reflector by Structure-Actuator Simultaneous optimization

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第4期57卷 728-739页

作者： Satou, Yasutaka Furuya, Hiroshi Kogiso, Nozomu Japan Aerosp Explorat Agcy Dept Space Flight Syst Inst Space & Astronaut Sci Sagamihara Kanagawa 2528510 Japan Tokyo Inst Technol Dept Mech Engn Yokohama Kanagawa 2268502 Japan Osaka Prefecture Univ Dept Aerosp Engn Sakai Osaka 5998531 Japan

This paper proposes an efficient shape correction method for high-precision deformable reflectors. To this end, the specific error modes that affect the antenna performance are selectively corrected by simultaneous optimization of the structure and the actuator locations. First, a method to correct the specific error modes by design optimization is formulated, where a residual surface error vector is introduced to evaluate the contribution of each error mode. A constraint parameter for the actuator location, which helps thea* optimization algorithm find the global minimum, is proposed to improve the optimization performance. The results of the optimization indicated that the proposed constraint parameter as well as the simultaneous optimization of the structure and actuator distribution improve optimization performance. The optimized deformable reflector with the shape control actuators is then manufactured for experimental verification. The experimental results are in good agreement with the numerical results obtained in the design phase, thereby confirming the feasibility of the optimum design. Thus, the effectiveness of the proposed shape correction method is indicated.

关键词： Piezo Actuators Antenna Design Shape Control optimization algorithm Finite Element Analysis Particle Swarm optimization Electric Field Intensity Space Environment Numerical Simulation Institute of Space and Astronautical Science

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Multi-Objective optimization for Curvilinearly Stiffened Porous Sandwich Plates Reinforced with Graphene Nanoplatelets

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AIAA JOURNAL 2022年第12期60卷 6825-6841页

作者： Xiao, Yushan Wu, Zhen Zhang, Xinyu Ren, Xiaohui Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Xian Aeronaut Univ Sch Mech Engn Xian 710065 Peoples R China

With the development of innovative manufacturing technology, multi-objectivea* optimization algorithms for optimal design of advanced composite structures have gained increasing attention. An effective and high-accurate prediction on the mechanical behavior of structures is the basic core ofa* optimization algorithms. Thus, a novel refined sinusoidal higher-order theory (NRSHT) combined with isogeometric analysis (IGA) is developed as the high-precision solver. A novel curvilinearly stiffened porous sandwich plate reinforced with graphene nanoplatelets (CSP-GPL) is proposed as the research object. Compared with previous higher-order theories, the proposed NRSHT can more accurately forecast the natural frequencies of CSP-GPL through several numerical and experimental tests. Subsequently, the shape and material distribution design of CSP-GPL are studied with multi-objective optimization. The random forest regression (RFR) is utilized as the high-fidelity surrogate model to construct the objective function in the improved Nondominated Sorting Genetic algorithm (NSGA-II), which can significantly accelerate the integration of NRSHT-IGA and NSGA-II. Finally, the Pareto-optimal solutions, optimizing for fundamental frequency and total mass of CSP-GPL, are obtained from the present platform, which can give effective suggestions for the future designer to meet specific requirements.

关键词： Finite Element Analysis optimization algorithm Surrogate Model Composite Structures Genetic algorithm Mechanical Properties Nonlinear Vibrations Dynamic Response Abaqus Computer Aided Engineering

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Aerodynamic optimization of a Single-Stage Axial Compressor with Stator Shroud Air Injection

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AIAA JOURNAL 2017年第8期55卷 2739-2754页

作者： Dinh, Cong-Truong Ma, Sang-Bum Kim, Kwang-Yong Inha Univ Dept Mech Engn 100 Inha Ro Incheon 22212 South Korea

The effects of stator shroud air injection on aerodynamic performance were investigated for a single-stage axial compressor. Multiobjective optimization of the air injector was performed to maximize the enhancements of the peak adiabatic efficiency and stall margin using three-dimensional Reynolds-averaged Navier-Stokes equations. The numerical results for adiabatic efficiency and the total pressure ratio were validated with experimental data for a smooth casing. The design variables were determined through a parametric study as the axial distance between stator leading edge and the injector, the radius of the injector surface curvature, the circumferential coverage of the injector, and the injection mass flow rate. The peak adiabatic efficiency and stall margin were selected as the objective functions. Kriging models were constructed using objective function values calculated at 45 design points, which were selected by Latin hypercube sampling to approximate the objective functions. The performances of two differenta* optimization algorithms were compared. The optimization results showed that an efficiency-oriented optimum design presented an increase of 2.47% in the peak adiabatic efficiency when compared with the compressor with a smooth casing, whereas a stall margin-oriented optimum design showed an increase of 1.13% in the stall margin.

关键词： Multistage Axial Compressors Mass Flow Rate Reynolds Averaged Navier Stokes Aerodynamic Performance Leading Edges optimization algorithm Kriging Model Particle Swarm optimization Surrogate Model Sound Pressure Level

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Power optimization of Solar-Powered Aircraft with Specified Closed Ground Tracks

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JOURNAL OF AIRCRAFT 2013年第1期50卷 232-238页

作者： Spangelo, Sara C. Gilbert, Elmer G. Univ Michigan Ann Arbor MI 48109 USA

This paper considers the optimization of flight trajectories for solar-powered aircraft. This work is unique relative to past work because flight path is constrained to repeatedly traverse a specified closed ground path. Constraints of this form are of interest in a variety of missions where the goal is to loiter near a fixed point on the ground. The performance index to be maximized is the average input power to the battery over each cycle of the ground path. It is advantageous to allow the periodic flight path to have altitude variations because during both ascent and descent there are opportunities to increase the angle of sun exposure to the aircraft solar array. A novel procedure for solving the related optimization problem is described that addresses the implementation of a difficult state constraint: the flight path must belong to the surface of the vertical cylinder whose base is the closed ground path. Results for a wide collection of optimization examples are described, which lead to an important conclusion. By allowing aircraft speed and altitude to vary iris possible to obtain an average input battery power greater than the optimal power for constant speed and constant altitude.

关键词： Solar Powered Aircraft Flight Trajectory Aircraft Speed Level Flight Aerodynamic Coefficients Solar Power optimization algorithm Aircraft Trajectory Aircraft Altitude Flight Data

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Analysis and optimization of a V-Shape Combined Pole Interior Permanent-Magnet Synchronous Machine With Temperature Rise and Demagnetization Considered

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IEEE ACCESS 2021年 9卷 64761-64775页

作者： Wang, Mingqiao Zhu, Haotian Zhou, Chengyu Zheng, Ping Tong, Chengde Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

Interior permanent-magnet synchronous machines (IPMSMs) have the advantages of high efficiency, high power density and outstanding flux-weakening ability, which make them widely adopted in electric vehicles (EVs). Conventional IPMSMs using rare-earth permanent magnet (PM) materials face the problems of instable price and irreversible demagnetization under extreme operating conditions. To overcome these shortcomings, a V-shape combined pole IPMSM (VCP-IPMSM) is proposed, in which NdFeB PMs at the outer ends of V-shape pole are replaced by ferrite PMs. The improvement of the magnetic-pole scheme on air-gap flux density is analyzed, and the influences of the magnetic-pole geometries on electromagnetic performance are investigated. The thermal circuit model of VCP-IPMSM is established, and the demagnetization characteristics of the machine at different temperatures are analyzed, by which the thermal and demagnetization constraints of magnetic pole are obtained. The genetic algorithm based on response surface models is applied to optimize the magnetic-pole cost and electromagnetic torque simultaneously. Accordingly, with thermal and demagnetization constraints integrated with the optimization method, an optimum scheme is obtained. In comparison with the conventional IPMSM with single-material pole, the proposed VCP-IPMSM is proved to be able to promote the air-gap flux density quality, improve the anti-demagnetization ability at high temperature, and decrease the rare-earth PM consumption with the output torque remaining unchanged.

关键词： Magnetic flux Ferrites Demagnetization Coercive force Magnetic circuits Rotors Air gaps Combined pole V-shape pole interior permanent-magnet synchronous machine thermal circuit model demagnetization optimization algorithm

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