Abstract
Artificial intelligence techniques are excessively used in computing for training, forecasting and evaluation purposes. Among these techniques, artificial neural network (ANN) is widely used for developing prediction models. ANNs use various Meta-heuristic algorithms including approximation methods for training the neural networks. ANN plays a significant role in this area and can be helpful in determining the neural network input coefficient. The main goal of presented study is to train the neural network using meta-heuristic approaches and to enhance the perceptron neural network precision. In this article, we used an integrated algorithm to determine the neural network input coefficients. Later, the proposed algorithm was compared with other algorithms such as ant colony and invasive weed optimization for performance evaluation. The results reveal that the proposed algorithm results in more convergence with neural network coefficient as compared to existing algorithms. However the proposed method resulted in reduction of prediction error in the neural network.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Arnborg S, Proskurowski A (1989) Linear time algorithms for NP-hard problems restricted to partial k-trees. Discr Appl Math 23(1):11–24
Blum AL, Rivest RL (1992) Training a 3-node neural network is NP-complete. Neural Netw 5(1):117–127
Cao J, Zhang X, Zhang C et al (2020) Improved convolutional neural network combined with rough set theory for data aggregation algorithm. J Ambient Intell Human Comput 11:647–654. https://doi.org/10.1007/s12652-018-1068-9
Castellani M, Rowlands H (2009) Evolutionary Artificial Neural Network design and training for woodveneer classification. Eng Appl Artif Intell 22(4–5):732–741
Chen X (2020) The application of neural network with convolution algorithm in Western music recommendation practice. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01806-5
Crescenzi P, Kann V (1997) Approximation on the web: A compendium of NP optimization problems. International workshop on randomization and approximation techniques in computer science. Springer, Berlin, pp 111–118
Dangare CS, Apte SS (2012) Improved study of heart disease prediction system using data mining classification techniques. Int J Comput Appl 47(10):44–48
Dorigo M (1992) Optimization, learning and natural algorithms (in Italian), Ph.D. Thesis, Department of Electronics, The Polytechnic University of Milan, Italy
Fazli MS, Jean-Fabrice L (2013) A solution for forecasting pet chips prices for both short-term and long-term price forcasting using genetic programming. The 2013 International Conference on Artificial Intelligence, Paris, France
Fazli MS, Keykhosrow K, Saeed S (2013) Designing a hybrid neuro-fuzzy system for classifying the complex data, application on cornea transplant. In: Proceedings of International Conference on Artificial Intelligence, Las Vegas, USA
Gheisari M, Esnaashari M (2017) A survey to face recognition algorithms: advantageous and disadvantageous. J Mod Technol Eng 2(1):57–65
Gheisari M, Guojun W (2017) A survey on deep learning in big data. In: 15th IEEE/IFIP international conference on embedded and ubiquitous computing, Guangzhou
Gheisari M, AA Movassagh, Y Qin, J Yong, X Tao, J Zhang, H Shen (2016) NSSSD: a new semantic hierarchical storage for sensor data. In: IEEE 20th International conference on computer supported cooperative work in design, Nanchang, China
Giri R, Chowdhury A, Ghosh A, Das S, Abraham A, Snasel V (2010) A modified invasive weed optimization algorithm for training of feed-forward neural networks. In: 2010 IEEE international conference on systems man and cybernetics, Istanbul, Turkey
Green RC, Wang L, Alam M (2012) Training neural networks using central force optimization and particle swarm optimization: insights and comparisons. Expert Syst Appl 39(1):555–563
Han L, He X (2007) A novel opposition-based particle swarm optimization for noisy problems. In: IEEE 3rd international conference on natural computation, Hiakou, Hainan, China
Iqbal M, Hock BL, Wenqiang W, Yuxia Y (2009) A service oriented model for semantics-based data management in wireless sensor networks. In: IEEE international conference on advanced information networking and applications workshops, Bradford, UK
Khan A, Shah R, Imran M et al (2019) An alternative approach to neural network training based on hybrid bio meta-heuristic algorithm. J Ambient Intell Hum Comput 10:3821–3830. https://doi.org/10.1007/s12652-019-01373-4
Kiranyaz S, Ince T, Yildirim A, Gabbouj M (2009) Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks 22(10):1448–1462
Kröse B, Krose B, van der Smagt P, Smagt P (1993). An introduction to neural networks
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inpired from weed colonization. Ecol Inf 1(4):355–366
Melo H, Watada J (2016) Gaussian-PSO with fuzzy reasoning based on structural learning for training a neural network. Neurocomputing 172:405–412
Omran MGH (2009) Using opposition-based learning with particle swarm optimization and barebones differential evolution. Particle Swarm Optimization, InTech Education and Publishing, A. Lazinica (Ed), pp 373–384
Pradhan M, Sahu RK (2011) Predict the onset of diabetes disease using Artificial Neural Network (ANN). Int J Comput Sci Emerg Technol 2(2):2044–6004
Socha K, Blum C (2007) An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 16(3):235–247
Soni J, Ansari U, Sharma D, Soni S (2011) Intelligent and effective heart disease prediction system using weighted associative classifiers. Int J Comput Sci Eng 3(6):2385–2392
Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Talbi EG (2009) Metaheuristic: from design to implementation. Wiley, Amsterdam
Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference computational intelligence modeling control and automation, Vienna, Austria
Vosniakos GC, Benardos PG (2007) Optimizing feedforward artificial neural network architecture. Eng Appl Artif Intell 20(3):365–382
Wang L, Zou F, Hei X, Yang D, Chen D, Jiang Q (2014) An improved teaching–learning-based optimization with neighborhood search for applications of ANN. Neurocomputing 143:231–247
Wu Z, Ni Z, Zhang C, Gu L (2008) Opposition based comprehensive learning particle swarm optimization. In: 3th International Conference on Intelligent System and Knowledge Engineering, China
Yaghini M, Khoshraftar MM, Fallahi M (2011) HIOPGA: a new hybrid metaheuristic algorithm to train feedforward neural networks for prediction. In: The 7th International Conference on Data Mining (DMIN’11), 2011, Las Vegas, NV, USA
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Movassagh, A.A., Alzubi, J.A., Gheisari, M. et al. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Human Comput 14, 6017–6025 (2023). https://doi.org/10.1007/s12652-020-02623-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02623-6