Experimental crack identification of API X70 steel pipeline using improved artificial neural networks based on whale optimization algorithm

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

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Comparative evaluation of the whale optimization algorithm and backpropagation for training neural networks to model soil wind erodibility

Arabian Journal of Geosciences ◽

10.1007/s12517-020-06328-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Bijan Raei ◽

Abbas Ahmadi ◽

Mohammad Reza Neyshaburi ◽

Mohammad Ali Ghorbani ◽

Farrokh Asadzadeh

Keyword(s):

Neural Networks ◽

Optimization Algorithm ◽

Comparative Evaluation ◽

Whale Optimization Algorithm ◽

Model Soil ◽

Whale Optimization

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

Scientific Programming ◽

10.1155/2018/1435810 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 4

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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