A new approach to solve multi-response statistical optimization problems using neural network, genetic algorithm, and goal attainment methods

2014 ◽  
Vol 75 (5-8) ◽  
pp. 1149-1162 ◽  
Author(s):  
Seyed Hamid Reza Pasandideh ◽  
Seyed Taghi Akhavan Niaki ◽  
Seyed Mahdi Atyabi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Goal Attainment ◽  
Optimization Problems ◽  
Statistical Optimization ◽  
New Approach

scholarly journals Hybrid Integrated System for Air Bending Optimal Design

2015 ◽  
Vol 1 (1) ◽  
pp. 30
Author(s):  
Jaber Eid Abu Qudeiri ◽  
Fayiz Abu Khadra ◽  
Usama Umer
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Metal Forming ◽  
Optimization Problems ◽  
Fe Analysis ◽  
Integrated System ◽  
Computational Time ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Search Spaces

Genetic algorithm (GA) is widely accepted method for handling optimization problems. GA can find optimal solutions for large and irregular search spaces. However, finding optimal solutions using GA is associated with high computational time when coupled with finite element (FE) code, since FE analysis should be applied to each individual of GA populations. A neural network metamodel (NNM) is introduced to reduce the computational time.GA utilizes the NNMas an approximation tool instead of FE. Application examples results show that the metamodelcan be used efficiently to obtainthe optimal process parameters of metal forming operations with large saving in time.

Application of a Shell-Spring Model for the Optimization of Radial Tire Contour Using a Genetic Algorithm

2003 ◽  
Vol 31 (1) ◽  
pp. 39-63 ◽  
Author(s):  
G. Unnithan ◽  
R. KrishnaKumar ◽  
A. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimization Problems ◽  
Optimization Procedure ◽  
Element Model ◽  
Spring Model ◽  
New Approach ◽  
Shell Elements ◽  
Complex Optimization ◽  
Profile Optimization

Abstract Optimization gives a new facet to design and development of tires. A new approach to the tire profile optimization is proposed in this study. The optimization procedure is integrated with a simple shell-spring finite element model for faster evaluation. In the shell-spring model, the shell elements represent the tire carcass, whereas the tread is represented by the spring elements. This is applied for the optimization of the tire contour for better maneuverability. The genetic algorithm, an evolutionary optimization procedure that is robust and efficient in solving complex optimization problems, is chosen. A new tire contour is obtained that improves tire maneuverability by increasing the sidewall belt tension.

Application of Improved Combined Shape Genetic Algorithm on Structural Optimization

2014 ◽  
Vol 602-605 ◽  
pp. 3119-3122
Author(s):  
Jun Xie ◽  
Jie Yan ◽  
Jing Yu Zhang ◽  
Yong Feng Xu ◽  
Meng Chen
Keyword(s):  
Genetic Algorithm ◽  
Structural Optimization ◽  
Optimization Problems ◽  
Design Method ◽  
Hybrid Genetic Algorithm ◽  
Initial Point ◽  
Shape Operator ◽  
Constrained Optimization Problems ◽  
New Approach ◽  
Optimal Design Method

A new approach to the generation of an initial point is proposed for discrete combined shape, which improves fully the local searching capability of discrete combined shape algorithm. Combined shape algorithm is embedded into genetic algorithm as a combined shape operator. Consequently a hybrid genetic algorithm for structural optimization with discrete variables is proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that improved combined shape genetic algorithm for structural optimization with discrete variable problems has a faster convergence speed, which has advantages of local searching capability and globally searching capability of genetic algorithm. Improved combined shape genetic algorithm is an efficient optimal design method for engineering structure.

scholarly journals A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
P.-Y. Chen ◽  
C.-H. Chen ◽  
H. Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Computation Time ◽  
General Purpose ◽  
Optimization Process ◽  
Electromagnetic Properties ◽  
Design Parameters ◽  
Pass Filter ◽  
Low Pass Filter

This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

scholarly journals Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zi Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Optimization Problems ◽  
Local Optimization ◽  
Bp Algorithm ◽  
Control Group ◽  
Intelligent Optimization ◽  
English Teaching ◽  
Optimization System

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class.

MULTI-OBJECTIVE OPTIMIZATION IN RELIABILITY SYSTEM USING GENETIC ALGORITHM AND NEURAL NETWORK

2008 ◽  
Vol 25 (05) ◽  
pp. 649-672 ◽  
Author(s):  
LIANG-HSUAN CHEN ◽  
CHENG-HSIUNG CHIANG
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
System Optimization ◽  
Evolutionary Strategy ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Reliability Systems

To optimize the design of reliability systems, an analyst is frequently faced with the demand of achieving several targets (i.e., maximization of system reliability, minimizations of cost, volume, and weight), some of which may be in conflict with each other. This paper presents a novel hybrid approach, combining a multi-objective genetic algorithm and a neural network, for multi-objective optimization of a reliability system, namely GANNRS (Genetic Algorithm and Neural Network for Reliability System optimization). The multi-objective genetic algorithm's evolutionary strategy is based on the modified neighborhood design, and is presented to find the Pareto optimal solutions so as to provide a variety of compromise solutions to the decision makers. The purpose of the neural network is to generate a good initial population in order to speed up the searching by genetic algorithm. For demonstrating the feasibility of the proposed approach, four multi-objective optimization problems of reliability system are used, and the outcomes are compared with those from other methods. The evidence shows that the proposed GANNRS is more efficient in computation, and the results from the objectives are appealing.

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