Research of Intelligent Control of Traffic Signal

This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used to evaluate performance of the hybridized training algorithm and compare them with MAPEs from Backpropagation, GA, and PSO. Yearly average MAPE and the average MAPEs for weekdays, Mondays, weekends, Holidays, and Bridging holidays show that PSO+GA algorithm outperforms other training algorithms for STLF.

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Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting

10.4018/978-1-6684-2408-7.ch010 ◽

2022 ◽

pp. 227-241

Author(s):

Kuruge Darshana Abeyrathna ◽

Chawalit Jeenanunta

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Particle Swarm Optimization ◽

Artificial Neural Networks ◽

Load Forecasting ◽

Training Algorithm ◽

Local Minima ◽

Short Term ◽

Swarm Optimization ◽

Short Term Load Forecasting

This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used to evaluate performance of the hybridized training algorithm and compare them with MAPEs from Backpropagation, GA, and PSO. Yearly average MAPE and the average MAPEs for weekdays, Mondays, weekends, Holidays, and Bridging holidays show that PSO+GA algorithm outperforms other training algorithms for STLF.

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Control scheme formulation for a parabolic trough collector using inverse artificial neural networks and particle swarm optimization

Journal of the Brazilian Society of Mechanical Sciences and Engineering ◽

10.1007/s40430-021-02862-4 ◽

2021 ◽

Vol 43 (4) ◽

Author(s):

M. Cervantes-Bobadilla ◽

J. A. Hernández-Pérez ◽

D. Juárez-Romero ◽

A. Bassam ◽

J. García-Morales ◽

...

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Artificial Neural Networks ◽

Particle Swarm ◽

Parabolic Trough ◽

Swarm Optimization ◽

Parabolic Trough Collector ◽

Control Scheme ◽

Artificial Neural

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Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization

IEEE Transactions on Magnetics ◽

10.1109/tmag.2015.2488901 ◽

2016 ◽

Vol 52 (3) ◽

pp. 1-4 ◽

Cited By ~ 25

Author(s):

Sebastien Martin ◽

Charles T. M. Choi

Keyword(s):

Neural Networks ◽

Particle Swarm Optimization ◽

Artificial Neural Networks ◽

Electrical Impedance Tomography ◽

Electrical Impedance ◽

Particle Swarm ◽

Impedance Tomography ◽

Swarm Optimization ◽

Tomography Reconstruction ◽

Artificial Neural

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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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