Training Feedforward Neural Networks Using Social Learning Particle Swarm Optimization- A Case Comparison Study on Electrical System

The knowledge about training feed forward neural networks (FNNs) is an important and complex issue in the supervised learning field. In the process of learning, the FNNs system involves some input parameters such as connection weights and biases, which may greatly influence the performance of FNNs training. In this paper, a newly developed meta-heuristic method, named social learning particle swarm optimization (SLPSO), is trying to find the optimal combination of connection weights and biases for FNNs, which is often used to deal with power load forecasting problem. In the numerical experiments, a case on the power load forecasting problem is employed to verify the effectiveness of SLPSO. The experiment results indicate that SLPSO has the advantages on the training accuracy and testing accuracy with respect to other six state-of-the-art intelligent optimization algorithms.

[1]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[2]  Khulood AlYahya,et al.  Artificial Bee Colony Training of Neural Networks , 2013, NICSO.

[3]  Wei-Yuan Lin,et al.  Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service , 2013 .

[4]  Tichun Wang,et al.  BP-GA Data Fusion Algorithm Studies Oriented to Smart Home , 2016 .

[5]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[6]  Hao Gao,et al.  A New Particle Swarm Algorithm and Its Globally Convergent Modifications , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Alireza Rezazadeh,et al.  Artificial neural network training using a new efficient optimization algorithm , 2013, Appl. Soft Comput..

[8]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).

[9]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[10]  Rosni Abdullah,et al.  Training Feed-Forward Neural Networks Using a Parallel Genetic Algorithm with the Best Must Survive Strategy , 2010, 2010 International Conference on Intelligent Systems, Modelling and Simulation.

[11]  Cheng-Jian Lin,et al.  A self-adaptive quantum radial basis function network for classification applications , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[12]  Yuyao He,et al.  Chaotic simulated annealing with decaying chaotic noise , 2002, IEEE Trans. Neural Networks.

[13]  Lifeng Xi,et al.  An Improved Particle Swarm Optimization for Evolving Feedforward Artificial Neural Networks , 2007, Neural Processing Letters.

[14]  Alok Singh,et al.  A Multi-objective PSO (MOPSO) algorithm for optimal active power dispatch with pollution control , 2017 .

[15]  Radha Krishna Prasad,et al.  Investigation on heat transfer characteristics of roughened solar air heater using ANN technique , 2018 .

[16]  Xiao Jun.  Application of Genetic Algorithms , 2005 .

[17]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[18]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[19]  Zhuang Xin-yan.  Application of Support Vector Machine Optimized by Genetic Algorithm in Electric load Prediction , 2012 .

[20]  Ali Safa Sadiq,et al.  Magnetic Optimization Algorithm for training Multi Layer Perceptron , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[21]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[22]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[23]  Lale Özbakir,et al.  Training neural networks with harmony search algorithms for classification problems , 2012, Eng. Appl. Artif. Intell..

[24]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[26]  W. Kinsner,et al.  Chaotic simulated annealing in multilayer feedforward networks , 1996, Proceedings of 1996 Canadian Conference on Electrical and Computer Engineering.

[27]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[28]  Nor Ashidi Mat Isa,et al.  Clustered-Hybrid Multilayer Perceptron network for pattern recognition application , 2011, Appl. Soft Comput..

[29]  A. M. S. Tekanyi,et al.  A Hybrid Model of PSO Algorithm and Artificial Neural Network for Automatic Follicle Classification , 2017 .

[30]  R. Storn,et al.  Differential Evolution , 2004 .

[31]  Andrzej Rusiecki,et al.  Training Neural Networks on Noisy Data , 2014, ICAISC.

[32]  Qin Yang,et al.  An Improved Particle Swarm Optimization , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[33]  Jun Sun,et al.  A global search strategy of quantum-behaved particle swarm optimization , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[34]  Andries Petrus Engelbrecht,et al.  Training feedforward neural networks with dynamic particle swarm optimisation , 2012, Swarm Intelligence.