Form error evaluation of ultra-precision freeform surfaces using genetic algorithm joint with sequential quadratic programming

2015 ◽  
pp. 2317-2322
Author(s):  
J Zhou ◽  
Y Li ◽  
Y Ji
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Error Evaluation ◽  
Form Error ◽  
Freeform Surfaces ◽  
Ultra Precision

An Integrated Approach for Optimization of Honeycomb Sandwich Structure under Impact Load

2012 ◽  
Vol 152-154 ◽  
pp. 1717-1722
Author(s):  
Hamdan Ajmal Khan ◽  
Faizan Habib Vance ◽  
Asif Israr ◽  
Tanzeel Ur Rehman
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Sandwich Structure ◽  
Impact Load ◽  
Sheet Thickness ◽  
Integrated Approach ◽  
Buckling Stress ◽  
Core Height ◽  
Honeycomb Sandwich Structure

In this paper weight optimization of sandwich structure consisting of a honeycomb core sandwiched between two layers is presented through the use of Sequential Quadratic Programming & Genetic Algorithm by constraining of certain parameters such as buckling stress, cost and geometry. The variables to be optimized are core height, face sheet thickness and cell thickness for an effective design and better performance of the entire structural system. Sequential Quadratic Programming in Matlaband Genetic Algorithm technique with high robustness is performed and comparison between the two results is made for early convergence of the variables to be optimized. In this way, the structure could easily be monitored for any volatility, and avoid probable failure by employing proper remedial action.

Genetic algorithm-based form error evaluation

2007 ◽  
Vol 18 (7) ◽  
pp. 1818-1822 ◽  
Author(s):  
Changcai Cui ◽  
Bing Li ◽  
Fugui Huang ◽  
Rencheng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Error Evaluation ◽  
Form Error

Study on Multidisciplinary Design Optimization of 3-RRS Parallel Robot

2012 ◽  
Vol 214 ◽  
pp. 919-923
Author(s):  
Jing Zhang ◽  
Bai Lin Li
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Design Optimization ◽  
Sequential Quadratic Programming ◽  
Multidisciplinary Design Optimization ◽  
Main Idea ◽  
Parallel Robot ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Robot System

The paper aims to apply the idea of multidisciplinary design optimization to the design of robot system. The main idea of collaborative optimization is introduced. The collaborative optimization frame of 3-RRS parallel robot is analyzed. With the method of genetic algorithm and Sequential Quadratic Programming, the investigation is made on the executing collaborative optimization of working stroke, driving performance and hydraulic components. The numerical results indicate that the collaborative optimization can be successfully applied to dealing with the complex robot system, and lay a foundation to solve more complex mechanical system.

Ocean Wave Spectral Estimation Using Vessel Wave Frequency Motions

2006 ◽  
Vol 129 (2) ◽  
pp. 90-96 ◽  
Author(s):  
R. Pascoal ◽  
C. Guedes Soares ◽  
A. J. Sørensen
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Spectral Estimation ◽  
Ocean Wave ◽  
Wave Frequency ◽  
Wave Spectra ◽  
Cost Functional ◽  
Advance Speed

Wave spectra are estimated from wave frequency motions of a vessel at zero or low advance speed. Minimization of a cost functional that indicates how well the estimated spectrum results in the measured motion spectra was based on sequential quadratic programming and a genetic algorithm. Two procedures have been developed and applied to numerically simulated motions of a 59 m length offshore supply vessel.

scholarly journals Capacitors placement in distribution systems with nonlinear load by using the variables’ inclusion and interchange algorithm

DYNA ◽  
2021 ◽  
Vol 88 (217) ◽  
pp. 13-22
Author(s):  
Ignacio Perez Abril
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Sequential Quadratic Programming ◽  
Distribution Systems ◽  
Harmonic Distortion ◽  
Linear Constraint ◽  
Substantial Improvement ◽  
Nonlinear Load ◽  
Interchange Algorithm ◽  
Distortion Index

This work presents a substantial improvement of the variables’ inclusion and interchange algorithm (VIIA) for capacitors placement that considers circuits with harmonic distortion. Several load states are considered, and fixed and switched capacitors are employed in optimization. All the pertinent constraints of voltage magnitude, total harmonic distortion, individual harmonic distortion, and of overstress of capacitors are implemented. The here defined global harmonic-distortion index states the distance to the feasibility or the unfeasibility of a solution with respect the harmonic distortion constraints. The inclusion in the sequential quadratic programming sub-problem of an inequality linear constraint on this global harmonic-distortion index, allows the determining of solutions that comply with the harmonic distortion related constraints. A comparison of the solutions of various examples obtained by the presented method with the best solutions obtained by the Matlab’s genetic algorithm shows the effectiveness of this method.

Research on hybrid optimization and deep learning modeling method in the performance seeking control

2020 ◽  
Vol 234 (7) ◽  
pp. 1340-1355
Author(s):  
Qiangang Zheng ◽  
Haoying Chen ◽  
Yong Wang ◽  
Haibo Zhang ◽  
Zhongzhi Hu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Quadratic Programming ◽  
Optimization Algorithm ◽  
Sequential Quadratic Programming ◽  
Particle Swarm ◽  
Modeling Method ◽  
Hybrid Optimization ◽  
Swarm Optimization

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

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