scholarly journals Hybrid Approach of Genetic Algorithm and Learning Automata on Flexible Transfer System.

2001 ◽  
Vol 19 (5) ◽  
pp. 593-602 ◽  
Author(s):  
Toshio Fukuda ◽  
Isao Takagawa ◽  
Kosuke Sekiyama ◽  
Yasuhisa Hasegawa ◽  
Susumu Shibata ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Learning Automata ◽  
Hybrid Approach ◽  
Transfer System

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

A multi-objective optimization approach for simultaneously lightweighting and maximizing functional performance of vehicle body structure

2019 ◽  
Vol 234 (7) ◽  
pp. 2086-2102
Author(s):  
Dengfeng Wang ◽  
Shuang Wang ◽  
Chong Xie
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Hybrid Approach ◽  
Support Vector ◽  
Ideal Solution ◽  
Analysis Method ◽  
Multi Objective ◽  
Sensitivity Analysis Method ◽  
Order Preference ◽  
Pillar Structure

This study presents a hybrid approach to integrate the comprehensive sensitivity analysis method, support vector machine technology, modified non-dominated sorting genetic algorithm-II method and the technique for order preference by similarity to ideal solution, which have been applied to multi-objective lightweight optimization of the B-pillar structure of an automobile. First, numerical models of the static–dynamic stiffness and the crashworthiness performance of automobile are established and validated by experimental testing. Then, the comprehensive sensitivity analysis method is used to define the final optimization variables. Experimental design and support vector machine based surrogate model techniques are introduced to establish the approximate model; subsequently, the modified non-dominated sorting genetic algorithm-II algorithm is applied to the multi-objective lightweight optimization design of the B-pillar structure, and the non-dominated solution set is determined. The principal component analysis method is applied to determine the weight of each objective. Finally, the technique for order preference by similarity to ideal solution method is used to rank Pareto front from best to worst to obtain the optimal solution; furthermore, a comparison between the original model and optimized design denotes that the mass of the B-pillar being reduced by 22.55% under the other impacting indicators is well guaranteed. Therefore, the proposed hybrid approach provided promising prospects in the lightweight and crashworthiness optimization application of the B-pillar.

Sign in / Sign up

Export Citation Format

Share Document