Design Optimization by Integration of Evolutionary Search and Cumulative Approximation

2005 ◽  
Author(s):  
Kikuo Fujita ◽  
Tomoki Ushiro ◽  
Noriyasu Hirokawa
Keyword(s):  
Design Optimization ◽  
Computational Cost ◽  
Optimal Solution ◽  
Response Surfaces ◽  
Evolutionary Search ◽  
Design Problems ◽  
Real Coded Genetic Algorithm ◽  
Cumulative Function ◽  
Gradient Based ◽  
New Framework

This paper proposes a new design optimization framework by integrating evolutionary search and cumulative function approximation. While evolutionary algorithms are robust even under multi-peaks, rugged natures, etc., their computational cost is inferior to ordinary schemes such as gradient-based methods. While response surface techniques such as quadratic approximation can save computational cost for complicated design problems, the fidelity of solution is affected by density of samples. The new framework simultaneously performs evolutionary search and constructs response surfaces. That is, in its early phase the search is performed over roughly but globally approximated surfaces with the relatively small number of samples, and in its later phase the search is performed intensively around promising regions, which are revealed in the preceded phases, over response surfaces enhanced with additional samples. This framework is expected to be able to robustly find the optimal solution with less sampling. An optimization algorithm is implemented by combining a real-coded genetic algorithm and a Voronoi diagram based cumulative approximation, and it is applied to some numerical examples for discussing its potential and promises.

Reliability-Based Design Optimization of Complex Problems With Multiple Design Points via Narrowed Search Region

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Yutian Wang ◽  
Peng Hao ◽  
Zhendong Guo ◽  
Dachuan Liu ◽  
Qiang Gao
Keyword(s):  
Design Optimization ◽  
Risk Function ◽  
Computational Cost ◽  
Reliability Based Design Optimization ◽  
Search Region ◽  
Complex Problems ◽  
Reliability Based Design ◽  
Training Samples ◽  
Gradient Based ◽  
Similar Accuracy

Abstract The expensive computational cost is always a major concern for reliability-based design optimization (RBDO) of complex problems. The performance of RBDO can be lowered by the inaccuracy of reliability analysis (RA) which is caused by multiple local optimums and multiple design points in highly non-linear space. In order to reduce the computational burden and guarantee the accuracy of RA (and thus to improve the RBDO performance), a global RBDO algorithm by adopting an improved constraint boundary sampling (GRBDO-ICBS) method is proposed. Specifically, the GRBDO-ICBS method first narrows the concerned search region by using a Kriging-based global search. The accuracies of the design points are verified by the expected risk function (ERF), and the corresponding inaccurate design points are added into training samples to update Kriging. Then a multi-start gradient-based sequential RBDO is carried out, which tries to find out all multiple design points in the concerned search region. The performance of GRBDO-ICBS is demonstrated by four examples. All results have shown that the proposed method can achieve similar accuracy as Monte Carlo simulation (MCS)-based RBDO but with a much lower computational cost.

Reliability-Based Design Optimization for Design Problems with Random Fuzzy and Interval Uncertainties

2019 ◽  
Vol 17 (06) ◽  
pp. 1950018 ◽  
Author(s):  
Li-Xiang Zhang ◽  
Xin-Jia Meng ◽  
He Zhang
Keyword(s):  
Design Optimization ◽  
Mechanical Design ◽  
Computational Cost ◽  
Reliability Based Design Optimization ◽  
Design Problems ◽  
Main Obstacle ◽  
Reliability Based Design ◽  
Interval Uncertainties ◽  
Mixed Uncertainties ◽  
Measurement Approach

Reliability-based design optimization (RBDO) has been widely used in mechanical design. However, the treatment of various uncertainties and associated computational burden are still the main obstacle of its application. A methodology of RBDO under random fuzzy and interval uncertainties (RFI-RBDO) is proposed in this paper. In the proposed methodology, two reliability analysis approaches, respectively named as FORM-[Formula: see text]-URA and interpolation-based sequential performance measurement approach (ISPMA), are developed for the mixed uncertainties assessment, and a parallel-computing-based SOMUA (PCSOMUA) method is proposed to reduce the computational cost of RFI-RBDO. Finally, two examples are provided to verify the validity of the methods.

Design Optimization of Spur and Helical Gear Pairs

2015 ◽  
Vol 766-767 ◽  
pp. 1034-1043
Author(s):  
S. Sudhagar ◽  
V. Srinivasa Raman
Keyword(s):  
Design Optimization ◽  
Power Efficiency ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Helical Gear ◽  
Gear Train ◽  
Test Problems ◽  
Gear Pair ◽  
Real Coded Genetic Algorithm ◽  
Comparative Results

Gears are the most common of machine elements and due to that many studies have been conducted on optimum gear design. Gear optimization can be divided into two categories, namely, single gear pair or Gear train optimization. The problem of gear pairs design optimization is difficult to solve because it involves multiple objectives and large number of variables. Hence a trustworthy and resilient optimization technique will be more useful in obtaining an optimal solution for the problems. In the proposed work an effort has been made to optimize spur and helical gear pair design using LINGO and Meta heuristics algorithms like Real Coded Genetic Algorithm (RCGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).On applying the combined objective function factors like Power, Efficiency is maximized and the overall Weight, Centre distance has been minimized in the model. The performance of the proposed algorithms is validated through test problems and the comparative results are reported.

scholarly journals Aerostructural Design Exploration of a Wing in Transonic Flow

Aerospace ◽  
2020 ◽  
Vol 7 (8) ◽  
pp. 118
Author(s):  
Nicolas P. Bons ◽  
Joaquim R. R. A. Martins
Keyword(s):  
Design Optimization ◽  
Computational Cost ◽  
Optimization Procedure ◽  
Fine Tuning ◽  
Great Success ◽  
Wing Design ◽  
Design Problems ◽  
Low Speed ◽  
Fuel Burn ◽  
Speed Performance

Multidisciplinary design optimization (MDO) has been previously applied to aerostructural wing design problems with great success. Most previous applications involve fine-tuning a well-designed aircraft wing. In this work, we broaden the scope of the optimization problem by exploring the design space of aerostructural wing design optimization. We start with a rectangular wing and optimize the aerodynamic shape and the sizing of the internal structure to achieve minimum fuel burn on a transonic cruise mission. We use a multi-level optimization procedure to decrease computational cost by 40%. We demonstrate that the optimization can transform the rectangular wing into a swept, tapered wing typical of a transonic aircraft. The optimizer converges to the same wing shape when starting from a different initial design. Additionally, we use a separation constraint at a low-speed, high-lift condition to improve the off-design performance of the optimized wing. The separation constraint results in a substantially different wing design with better low-speed performance and only a slight decrease in cruise performance.

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.

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

Study on Mix-Variable Collaborative Design Optimization

2012 ◽  
Vol 215-216 ◽  
pp. 592-596
Author(s):  
Li Gao ◽  
Rong Rong Wang
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimization Algorithm ◽  
Collaborative Design ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Collaborative Optimization ◽  
Continuous Variables ◽  
Complex Product ◽  
Divide And Rule

In order to deal with complex product design optimization problems with both discrete and continuous variables, mix-variable collaborative design optimization algorithm is put forward based on collaborative optimization, which is an efficient way to solve mix-variable design optimization problems. On the rule of “divide and rule”, the algorithm decouples the problem into some relatively simple subsystems. Then by using collaborative mechanism, the optimal solution is obtained. Finally, the result of a case shows the feasibility and effectiveness of the new algorithm.

Determining Human Control Intent Using Inverse LQR Solutions

2013 ◽  
Author(s):  
M. Cody Priess ◽  
Jongeun Choi ◽  
Clark Radcliffe
Keyword(s):  
Human Subjects ◽  
Optimal Solution ◽  
Linear Matrix ◽  
Minimization Method ◽  
Human Control ◽  
Balance Task ◽  
Lqr Problem ◽  
Seated Balance ◽  
Gradient Based ◽  
Time Invariant

In this paper, we have developed a method for determining the control intention in human subjects during a prescribed motion task. Our method is based on the solution to the inverse LQR problem, which can be stated as: does a given controller K describe the solution to a time-invariant LQR problem, and if so, what weights Q and R produce K as the optimal solution? We describe an efficient Linear Matrix Inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown. Additionally, we propose a gradient-based, least-squares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. We develop a model for an upright seated-balance task which will be suitable for identification of human control intent once experimental data is available.

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Opeoluwa Owoyele ◽  
Pinaki Pal ◽  
Alvaro Vidal Torreira
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Design Optimization ◽  
Fuel Injection ◽  
A Priori ◽  
Computational Cost ◽  
Learning Approach ◽  
Data Generation ◽  
Compression Ignition Engine ◽  
Hyperparameter Selection

AbstractThe use of machine learning (ML)-based surrogate models is a promising technique to significantly accelerate simulation-driven design optimization of internal combustion (IC) engines, due to the high computational cost of running computational fluid dynamics (CFD) simulations. However, training the ML models requires hyperparameter selection, which is often done using trial-and-error and domain expertise. Another challenge is that the data required to train these models are often unknown a priori. In this work, we present an automated hyperparameter selection technique coupled with an active learning approach to address these challenges. The technique presented in this study involves the use of a Bayesian approach to optimize the hyperparameters of the base learners that make up a super learner model. In addition to performing hyperparameter optimization (HPO), an active learning approach is employed, where the process of data generation using simulations, ML training, and surrogate optimization is performed repeatedly to refine the solution in the vicinity of the predicted optimum. The proposed approach is applied to the optimization of a compression ignition engine with control parameters relating to fuel injection, in-cylinder flow, and thermodynamic conditions. It is demonstrated that by automatically selecting the best values of the hyperparameters, a 1.6% improvement in merit value is obtained, compared to an improvement of 1.0% with default hyperparameters. Overall, the framework introduced in this study reduces the need for technical expertise in training ML models for optimization while also reducing the number of simulations needed for performing surrogate-based design optimization.

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