scholarly journals Multi-Fidelity Local Surrogate Model for Computationally Efficient Microwave Component Design Optimization

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3023 ◽  
Author(s):  
Yiran Song ◽  
Qingsha S. Cheng ◽  
Slawomir Koziel
Keyword(s):  
Surrogate Model ◽  
Polynomial Interpolation ◽  
Computational Cost ◽  
Optimal Solution ◽  
Wide Band ◽  
Optimization Method ◽  
Local Region ◽  
Optimization Process ◽  
Optimized Design ◽  
Mimo Antenna

In order to minimize the number of evaluations of high-fidelity (“fine”) model in the optimization process, to increase the optimization speed, and to improve optimal solution accuracy, a robust and computational-efficient multi-fidelity local surrogate-model optimization method is proposed. Based on the principle of response surface approximation, the proposed method exploits the multi-fidelity coarse models and polynomial interpolation to construct a series of local surrogate models. In the optimization process, local region modeling and optimization are performed iteratively. A judgment factor is introduced to provide information for local region size update. The last local surrogate model is refined by space mapping techniques to obtain the optimal design with high accuracy. The operation and efficiency of the approach are demonstrated through design of a bandpass filter and a compact ultra-wide-band (UWB) multiple-in multiple-out (MIMO) antenna. The response of the optimized design of the fine model meet the design specification. The proposed method not only has better convergence compared to an existing local surrogate method, but also reduces the computational cost substantially.

scholarly journals Low-Cost Multi-Objective Optimization of Multiparameter Antenna Structures Based on the l1 Optimization BPNN Surrogate Model

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 839
Author(s):  
Dong ◽  
Qin ◽  
Mo
Keyword(s):  
Surrogate Model ◽  
Communication Systems ◽  
Low Cost ◽  
Computational Cost ◽  
Optimization Method ◽  
Antenna Design ◽  
Multi Objective Optimization ◽  
Compact Structure ◽  
Multi Objective ◽  
L1 Optimization

The development of modern wireless communication systems not only requires the antenna to be lightweight, low cost, easy to manufacture and easy to integrate but also imposes requirements on the miniaturization, wideband, and multiband design of the antenna. Therefore, designing an antenna that quickly and effectively meets multiple performance requirements is of great significance. To solve the problem of the large computational cost of traditional multi-objective antenna design methods, this paper proposes a backpropagation neural network surrogate model based on l1 optimization (l1-BPNN). The l1 optimization method tends to punish larger weight values and select smaller weight values so as to preserve a small amount of important weights and reset relatively unimportant weights to zero. By using l1 optimization method, the network mapping structure can be automatically adjusted to achieve the most suitable and compact structure of the surrogate model. Furthermore, for multi-parameter antenna design problems, a fast multi-objective optimization framework is constructed using the proposed l1-BPNN as a surrogate model. The framework is illustrated using a miniaturized multiband antenna design case, and a comparison with previously published methods, as well as numerical validation, is also provided.

Reduction in the aerodynamic drag around a generic vehicle by using a non-smooth surface

2016 ◽  
Vol 231 (1) ◽  
pp. 130-144 ◽  
Author(s):  
Yiping Wang ◽  
Cheng Wu ◽  
Gangfeng Tan ◽  
Yadong Deng
Keyword(s):  
Drag Reduction ◽  
Drag Coefficient ◽  
Surrogate Model ◽  
Smooth Surface ◽  
Aerodynamic Drag ◽  
Optimal Solution ◽  
Optimization Method ◽  
Aerodynamic Optimization ◽  
Design Variables ◽  
Aerodynamic Drag Coefficient

Numerical investigations are carried out to investigate the reduction in the aerodynamic drag of a vehicle by employing a dimpled non-smooth surface. The computational scheme was validated by the experimental data reported in literature. The mechanism and the effect of the dimpled non-smooth surface on the drag reduction were revealed by analysing the flow field structure of the wake. In order to maximize the drag reduction performance of the dimpled non-smooth surface, an aerodynamic optimization method based on a Kriging surrogate model was employed to design the dimpled non-smooth surface. Four structure parameters were selected as the design variables, and a 16-level design-of-experiments method based on orthogonal arrays was used to analyse the sensitivities and the influences of the variables on the drag coefficient; a surrogate model was constructed from these. Then a multi-island genetic algorithm was employed to obtain the optimal solution for the surrogate model. Finally, the surrogate model and the simulation results showed that the optimal combination of design variables can reduce the aerodynamic drag coefficient by 5.20%.

Exact-Gradient Optimization Method for Rigid-Body Guidance Synthesis of Planar Mechanisms

2004 ◽  
Author(s):  
Ramon Sancibrian ◽  
Pablo Garcia ◽  
Fernando Viadero ◽  
Alfonso Fernandez
Keyword(s):  
Rigid Body ◽  
Synthesis Method ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Method ◽  
Planar Mechanisms ◽  
Synthesis Conditions ◽  
Kinematic Chains ◽  
Gradient Optimization ◽  
Definition Of

In this paper an approximate kinematic synthesis method is presented with application to rigid-body guidance in planar multibody systems. The problem of finding the optimal dimensions in linkages with rigid-body guidance constraints has been widely studied. Many techniques have been developed and applied to numerous kinematic chains. However, some problems remain without appropriate solution, such as a large number of required poses or low computational cost. The proposed method uses exact-gradient determination to search for an optimal solution. The modelling of the mechanism uses fully Cartesian coordinates and is formulated by means of algebraic constraint equations. Furthermore, the formulation allows the use of a large number of prescribed poses giving high accuracy in the definition of synthesis conditions. Examples are included to illustrate the new approach to some synthesis specifications.

Optimized Design of the Horn of Ultrasonic Roll Welding

2012 ◽  
Vol 482-484 ◽  
pp. 2223-2226 ◽  
Author(s):  
Kuen Ming Shu ◽  
Yu Guang Li ◽  
Chun Chi Chan ◽  
Jonq Bor Kuan
Keyword(s):  
Random Search ◽  
Optimal Solution ◽  
Optimization Method ◽  
Resonant Mode ◽  
Mode Frequency ◽  
Optimized Design ◽  
Solar Panels ◽  
Design Variables ◽  
Local Optimal Solution ◽  
The Impact

Previous studies on the amplitude horn only calculated sizes in consistent with the axial resonant mode frequency and disc bending resonant mode frequency without considering the overall stress and the amplitude of the disc’s outer ring. The resonant frequency of the amplitude horn cannot occur around 35 kHz. Such a design results in the inability to weld and may damage solar panels or lead to poor welding quality. Using the optimization method to address these problems, the proposed design process in this study is to conduct sensitivity analysis by the gradient method to understand the impact of design variables on the objective function for the selection of design variables. Then, this study applied the random search method to find out the feasible design of arrays to optimize the structure of two arrays closest to the design objective by the full factorial experiment method to ensure to get the global optimal solution rather than the local optimal solution. Finally, by design examples, this study used the sub-problem approximation method to search the optimized solution and compared the differences of the two methods, in order to confirm whether the objective of optimized design of amplitude horn had been achieved.

Optimization Framework Using Surrogate Model for Aerodynamically Improved 3D Turbine Blade Design

2014 ◽  
Author(s):  
Sanga Lee ◽  
Saeil Lee ◽  
Kyu-Hong Kim ◽  
Dong-Ho Lee ◽  
Young-Seok Kang ◽  
...  
Keyword(s):  
Surrogate Model ◽  
Design Space ◽  
Computational Cost ◽  
Three Dimensional ◽  
Experimental Point ◽  
Optimized Design ◽  
Kriging Method ◽  
Baseline Design ◽  
Design Variables ◽  
Nonlinear Design

In simple optimization problem, direct searching methods are most accurate and practical enough. However, for more complicated problem which contains many design variables and demands high computational costs, surrogate model methods are recommendable instead of direct searching methods. In this case, surrogate models should have reliability for not only accuracy of the optimum value but also globalness of the solution. In this paper, the Kriging method was used to construct surrogate model for finding aerodynamically improved three dimensional single stage turbine. At first, nozzle was optimized coupled with base rotor blade. And then rotor was optimized with the optimized nozzle vane in order. Kriging method is well known for its good describability of nonlinear design space. For this reason, Kriging method is appropriate for describing the turbine design space, which has complicated physical phenomena and demands many design variables for finding optimum three dimensional blade shapes. To construct airfoil shape, Prichard topology was used. The blade was divided into 3 sections and each section has 9 design variables. Considering computational cost, some design variables were picked up by using sensitivity analysis. For selecting experimental point, D-optimal method, which scatters each experimental points to have maximum dispersion, was used. Model validation was done by comparing estimated values of random points by Kriging model with evaluated values by computation. The constructed surrogate model was refined repeatedly until it reaches convergence criteria, by supplying additional experimental points. When the surrogate model satisfies the reliability condition and developed enough, finding optimum point and its validation was followed by. If any variable was located on the boundary of design space, the design space was shifted in order to avoid the boundary of the design space. This process was also repeated until finding appropriate design space. As a result, the optimized design has more complicated blade shapes than that of the baseline design but has higher aerodynamic efficiency than the baseline turbine stage.

Multi-levels Kriging surrogate model-based robust aerodynamics optimization design method

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040115
Author(s):  
Neng Xiong ◽  
Yang Tao ◽  
Jun Lin ◽  
Xue-Qiang Liu
Keyword(s):  
Design Optimization ◽  
Surrogate Model ◽  
Optimization Design ◽  
Design Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Mean Value ◽  
Robust Design Optimization ◽  
Efficient Global Optimization ◽  
Kriging Surrogate Model

Robust design optimization has a great potential application in many engineering fields. In the conventional robust aerodynamics design optimization method, the main difficulty is expensive computational cost related to a large number of function evaluations for uncertainty quantification (UQ). To alleviate the expensive burden for UQ, two levels Kriging surrogate model was introduced. The first level is for the mean value and the second level is for the variances. Through the second level Kriging surrogate models, the method of Monte Carlo Simulation (MCS), which requires a huge number of function evaluations, can be effectively applied to the analysis of variance. Efficient Global Optimization algorithm (EGO) was employed to achieve the global optimized results. To validate the performance of the design method, both one-dimensional function and two-dimensional function were applied. Finally, robust aerodynamics design optimization was applied for a low-drag airfoil. The results show that the optimal solutions obtained from the uncertainty-based optimization formulation are less sensitive to uncertainties to small manufacturing errors.

Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function

2019 ◽  
Vol 13 (01) ◽  
pp. 5-23 ◽  
Author(s):  
Ying Fung Yiu ◽  
Jing Du ◽  
Rabi Mahapatra
Keyword(s):  
High Performance ◽  
Search Algorithm ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Method ◽  
Careful Consideration ◽  
Heuristic Function ◽  
Design And Optimization ◽  
Function Design ◽  
Time And Space Complexity

The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoid complex heuristic function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex heuristic function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics function (MWH) named Evolutionary Heuristic A* search (EHA*) to: (1) minimize the effort on heuristic function design via Genetic Algorithm (GA), (2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and (3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of heuristic design. We apply EHA* to multiple grid-based pathfinding benchmarks to evaluate the performance. Our experiment result shows that EHA* (1) is capable of choosing an accurate heuristic function that provides an optimal solution, (2) can identify and eliminate inefficient heuristics, (3) is able to automatically design multi-heuristics function, and (4) minimizes both the time and space complexity.

On the Optimal Precoder Design for Energy-Efficient and Secure MIMO Systems

2017 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Tung T. Vu ◽  
Ha Hoang Kha
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Search Algorithm ◽  
Multiple Input Multiple Output ◽  
Research Work ◽  
Computational Cost ◽  
Optimal Solution ◽  
Secrecy Rate ◽  
Highly Nonlinear ◽  
Precoder Design

In this research work, we investigate precoder designs to maximize the energy efficiency (EE) of secure multiple-input multiple-output (MIMO) systems in the presence of an eavesdropper. In general, the secure energy efficiency maximization (SEEM) problem is highly nonlinear and nonconvex and hard to be solved directly. To overcome this difficulty, we employ a branch-and-reduce-and-bound (BRB) approach to obtain the globally optimal solution. Since it is observed that the BRB algorithm suffers from highly computational cost, its globally optimal solution is importantly served as a benchmark for the performance evaluation of the suboptimal algorithms. Additionally, we also develop a low-complexity approach using the well-known zero-forcing (ZF) technique to cancel the wiretapped signal, making the design problem more amenable. Using the ZF based method, we transform the SEEM problem to a concave-convex fractional one which can be solved by applying the combination of the Dinkelbach and bisection search algorithm. Simulation results show that the ZF-based method can converge fast and obtain a sub-optimal EE performance which is closed to the optimal EE performance of the BRB method. The ZF based scheme also shows its advantages in terms of the energy efficiency in comparison with the conventional secrecy rate maximization precoder design.

scholarly journals Updated Kriging-Assisted Shape Optimization of a Gravity Dam

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma
Keyword(s):  
Surrogate Model ◽  
Computational Cost ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Optimal Shape ◽  
Small Sample ◽  
Gravity Dam ◽  
Kriging Surrogate Model ◽  
Gravity Dams ◽  
Sample Set

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.

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