A Neural-Network-Assisted Optimization Framework and Its Use for Optimum-Parameter Identification

2006 ◽  
pp. 221-235 ◽  
Author(s):  
Tapabrata Ray
Keyword(s):  
Neural Network ◽  
Design Optimization ◽  
Optimum Parameter ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimum Process ◽  
Optimization Framework ◽  
Computationally Expensive ◽  
Expensive Process ◽  
Low Computational Cost

Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In this chapter, a framework for design optimization is introduced that makes use of neural-network-based surrogates in lieu of actual analysis to arrive at optimum process parameters. The performance of the algorithm is studied using a number of mathematical benchmarks to instill confidence on its performance before reporting the results of a springback minimization problem. The results clearly indicate that the framework is able to report optimum designs with a substantially low computational cost while maintaining an acceptable level of accuracy.

Web application to support evidence of individual emotional impact evoked by COVID-19 pandemic restrictions (Preprint)

2021 ◽  
Author(s):  
Hugo Mitre-Hernandez ◽  
Rodolfo Ferro-Perez ◽  
Francisco Gonzalez-Hernandez
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Web Application ◽  
Deep Neural Network ◽  
Data Transfer ◽  
Computational Cost ◽  
Low Computational Cost ◽  
The Web

BACKGROUND Mental health effects during COVID-19 quarantine need to be handled because patients, relatives, and healthcare workers are living with negative emotional behaviors. The clinical disorders of depression and anxiety are evoking anger, fear, sadness, disgust, and reducing happiness. Therefore, track emotions with the help of psychologists on online consultations –to reduce the risk of contagion– will go a long way in assisting with mental health. The human micro-expressions can describe genuine emotions of people and can be captured by Deep Neural Networks (DNNs) models. But the challenge is to implement it under the poor performance of a part of society's computers and the low speed of internet connection. OBJECTIVE This study aimed to create a useful and usable web application to record emotions in a patient’s card in real-time, achieving a small data transfer, and a Convolutional Neural Networks (CNN) model with a low computational cost. METHODS To validate the low computational cost premise, firstly, we compare DNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network (ResmoNet) model. Secondly, we compare the trained models' results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion recognition (RTE). Finally, we design a data transfer that includes the raw data of emotions and the basic text information of the patient. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists (experts). RESULTS All CNN models were set up using 150 epochs for training and testing comparing the results for each variable in ResmoNet with the best model. It was obtained that ResmoNet has 115,976 NP less than MobileNet, 243,901 FLOPS less than MobileNet, and 5% less accuracy than EDNN (95%). Moreover, ResmoNet used less MMU than any model, only EDNN overcomes ResmoNet in 0.01 seconds for RTE. Finally, with our model, we develop a web application to collect emotions in real-time during a psychological consultation. For data transfer, the patient’s card and raw emotional data have 2 kb with a UTF-8 encoding approximately. Finally, according to the experts, the web application has good usability (73.8 of 100) and utility (3.94 of 5). CONCLUSIONS A usable and useful web application for psychologists and psychiatrists is presented. This tool includes an efficient and light facial emotion recognition model. Its purpose is to be a complementary tool for diagnostic processes.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Control and Design Optimization of a Novel Hybrid Electric Powertrain System

2017 ◽  
Author(s):  
Chenyu Yi ◽  
Bogdan Epureanu
Keyword(s):  
Optimal Control ◽  
Design Optimization ◽  
Fuel Economy ◽  
Optimization Problems ◽  
Computational Cost ◽  
Electric Machines ◽  
Hybrid Electric ◽  
Hybrid Electric Powertrain ◽  
And Control ◽  
Electric Powertrains

Control and design optimization of hybrid electric powertrains is necessary to maximize the benefits of novel architectures. Previous studies have proposed multiple optimal and near-optimal control methods, approaches for design optimization, and ways to solve coupled design and control optimization problems for hybrid electric powertrains. This study presents control and design optimization of a novel hybrid electric powertrain architecture to evaluate its performance and potential using physics-based models for the electric machines, the battery and a near-optimal control, namely the equivalent consumption minimization strategy. Design optimization in this paper refers to optimizing the sizes of the powertrain components, i.e. electric machines, battery and final drive. The control and design optimization problem is formulated using nested approach with sequential quadratic programming as design optimization method. Metamodeling is applied to abstract the near-optimal powertrain control model to reduce the computational cost. Fuel economy, sizes of components, and consistency of city and highway fuel economy are reported to evaluate the performance of the powertrain designs. The results suggest an optimal powertrain design and control that grants good performance. The optimal design is shown to be robust and non-sensitive to slight component size changes when evaluated for the near-optimal control.

scholarly journals Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach

2020 ◽  
Vol 10 (7) ◽  
pp. 2223 ◽  
Author(s):  
J. C. Hsiao ◽  
Kumar Shivam ◽  
C. L. Chou ◽  
T. Y. Kam
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Shape Design ◽  
Optimization Approach ◽  
Robot Arm ◽  
Shape Design Optimization ◽  
Robot Arms ◽  
Conflicting Objectives ◽  
Computer Aided

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.

The Influence of Metamodeling Techniques on the Multidisciplinary Design Optimization of a Radial Compressor Impeller

2012 ◽  
Author(s):  
Christopher Chahine ◽  
Joerg R. Seume ◽  
Tom Verstraete
Keyword(s):  
Fluid Dynamics ◽  
Design Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Computational Cost ◽  
Computational Time ◽  
Radial Compressor ◽  
Compressor Impeller ◽  
The Impact ◽  
Low Computational Cost

Aerodynamic turbomachinery component design is a very complex task. Although modern CFD solvers allow for a detailed investigation of the flow, the interaction of design changes and the three dimensional flow field are highly complex and difficult to understand. Thus, very often a trial and error approach is applied and a design heavily relies on the experience of the designer and empirical correlations. Moreover, the simultaneous satisfaction of aerodynamic and mechanical requirements leads very often to tedious iterations between the different disciplines. Modern optimization algorithms can support the designer in finding high performing designs. However, many optimization methods require performance evaluations of a large number of different geometries. In the context of turbomachinery design, this often involves computationally expensive Computational Fluid Dynamics and Computational Structural Mechanics calculations. Thus, in order to reduce the total computational time, optimization algorithms are often coupled with approximation techniques often referred to as metamodels in the literature. Metamodels approximate the performance of a design at a very low computational cost and thus allow a time efficient automatic optimization. However, from the experiences gained in past optimizations it can be deduced that metamodel predictions are often not reliable and can even result in designs which are violating the imposed constraints. In the present work, the impact of the inaccuracy of a metamodel on the design optimization of a radial compressor impeller is investigated and it is shown if an optimization without the usage of a metamodel delivers better results. A multidisciplinary, multiobjective optimization system based on a Differential Evolution algorithm is applied which was developed at the von Karman Institute for Fluid Dynamics. The results show that the metamodel can be used efficiently to explore the design space at a low computational cost and to guide the search towards a global optimum. However, better performing designs can be found when excluding the metamodel from the optimization. Though, completely avoiding the metamodel results in a very high computational cost. Based on the obtained results in present work, a method is proposed which combines the advantages of both approaches, by first using the metamodel as a rapid exploration tool and then switching to the accurate optimization without metamodel for further exploitation of the design space.

Multidisciplinary Design Optimization of Modular Industrial Robots

2011 ◽  
Author(s):  
Mehdi Tarkian ◽  
Johan Persson ◽  
Johan O¨lvander ◽  
Xiaolong Feng
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Structural Strength ◽  
Computational Cost ◽  
Industrial Robots ◽  
Multidisciplinary Design ◽  
Geometry Model ◽  
Optimization Framework ◽  
Speed Up ◽  
Multi Level

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model. The proposed framework utilizes well-established methods such as metamodeling and multi-level optimization in order to speed up the design optimization process. The contribution of the paper is to show that by applying a merger of well-established methods, the computational cost can be cut significantly, enabling search for truly novel concepts.

Multi-Objective Optimization With Multiple Spatially Distributed Surrogates

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Kalyan Shankar Bhattacharjee ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Optimization Problems ◽  
A Priori ◽  
Search Space ◽  
Nsga Ii ◽  
Multi Objective ◽  
Engineering Design Optimization ◽  
Spatially Distributed ◽  
Computationally Expensive

In engineering design optimization, evaluation of a single solution (design) often requires running one or more computationally expensive simulations. Surrogate assisted optimization (SAO) approaches have long been used for solving such problems, in which approximations/surrogates are used in lieu of computationally expensive simulations during the course of search. Existing SAO approaches often use the same type of approximation model to represent all objectives and constraints in all regions of the search space. The selection of a type of surrogate model over another is nontrivial and an a priori choice limits flexibility in representation. In this paper, we introduce a multi-objective evolutionary algorithm (EA) with multiple adaptive spatially distributed surrogates. Instead of a single global surrogate, local surrogates of multiple types are constructed in the neighborhood of each offspring solution and a multi-objective search is conducted using the best surrogate for each objective and constraint function. The proposed approach offers flexibility of representation by capitalizing on the benefits offered by various types of surrogates in different regions of the search space. The approach is also immune to illvalidation since approximated and truly evaluated solutions are not ranked together. The performance of the proposed surrogate assisted multi-objective algorithm (SAMO) is compared with baseline nondominated sorting genetic algorithm II (NSGA-II) and NSGA-II embedded with global and local surrogates of various types. The performance of the proposed approach is quantitatively assessed using several engineering design optimization problems. The numerical experiments demonstrate competence and consistency of SAMO.

Variable-fidelity shape optimization of dual-rotor wind turbines

2018 ◽  
Vol 35 (7) ◽  
pp. 2514-2542
Author(s):  
Andrew Thelen ◽  
Leifur Leifsson ◽  
Anupam Sharma ◽  
Slawomir Koziel
Keyword(s):  
Design Optimization ◽  
Wind Turbines ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fast Variable ◽  
Efficient Design ◽  
Content Type ◽  
Optimization Framework ◽  
Derivative Free ◽  
Variable Fidelity

Purpose Dual-rotor wind turbines (DRWTs) are a novel type of wind turbines that can capture more power than their single-rotor counterparts. Because their surrounding flow fields are complex, evaluating a DRWT design requires accurate predictive simulations, which incur high computational costs. Currently, there does not exist a design optimization framework for DRWTs. Since the design optimization of DRWTs requires numerous model evaluations, the purpose of this paper is to identify computationally efficient design approaches. Design/methodology/approach Several algorithms are compared for the design optimization of DRWTs. The algorithms vary widely in approaches and include a direct derivative-free method, as well as three surrogate-based optimization methods, two approximation-based approaches and one variable-fidelity approach with coarse discretization low-fidelity models. Findings The proposed variable-fidelity method required significantly lower computational cost than the derivative-free and approximation-based methods. Large computational savings come from using the time-consuming high-fidelity simulations sparingly and performing the majority of the design space search using the fast variable-fidelity models. Originality/value Due the complex simulations and the large number of designable parameters, the design of DRWTs require the use of numerical optimization algorithms. This work presents a novel and efficient design optimization framework for DRWTs using computationally intensive simulations and variable-fidelity optimization techniques.

scholarly journals Rapid Design Optimization of Multi-Band Antennas by Means of Response Features

2017 ◽  
Vol 24 (2) ◽  
pp. 337-346 ◽  
Author(s):  
Sławomir Kozieł ◽  
Adrian Bekasiewicz
Keyword(s):  
Design Optimization ◽  
Computational Cost ◽  
Parameters Optimization ◽  
Nonlinear Dependence ◽  
Optimization Approach ◽  
Simultaneous Control ◽  
Characteristic Points ◽  
Feature Based ◽  
Low Computational Cost ◽  
Multi Band

AbstractThis work examines the reduced-cost design optimization of dual- and multi-band antennas. The primary challenge is independent yet simultaneous control of the antenna responses at two or more frequency bands. In order to handle this task, a feature-based optimization approach is adopted where the design objectives are formulated on the basis of the coordinates of so-called characteristic points (or response features) of the antenna response. Due to only slightly nonlinear dependence of the feature points on antenna geometry parameters, optimization can be attained at a low computational cost. Our approach is demonstrated using two antenna structures with the optimum designs obtained in just a few dozen of EM simulations of the respective structure.

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