Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures

2020 ◽  
pp. 113339
Author(s):  
H. Tran-Ngoc ◽  
S. Khatir ◽  
H. Ho-Khac ◽  
G. De Roeck ◽  
T. Bui-Tien ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Damage Detection ◽  
Optimization Algorithm ◽  
Composite Structures ◽  
Laminated Composite ◽  
Hybrid Metaheuristic ◽  
Laminated Composite Structures ◽  
Metaheuristic Optimization Algorithm ◽  
Artificial Neural

scholarly journals A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mehmet Hacibeyoglu ◽  
Mohammed H. Ibrahim
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Artificial Neural

Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feed-forward artificial neural networks training.

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