Efficient binary grasshopper optimization based neural network algorithm for bitcoin value prediction

Document Type : Research Paper


1 Department of CSE, Bannari Amman Institute of Technology, Erode, Tamilnadu, India

2 Department of CSE, Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu, India

3 Department of IT, PSG College of Technology, Coimbatore, Tamilnadu, India


Digital currency plays a vital role in the process of trading as it helps the sellers and buyers to earn more profit. In today’s world, many categories of cryptocurrencies exist and each one of them employs its own security algorithms. Bitcoin price prediction is a complex problem that needs advanced algorithms to solve exactly. In this paper, swarm-based intelligence algorithms are applied in order to solve the bitcoin value prediction problem. In particular, Ant Colony Optimization and Binary Grasshopper Optimization algorithms are combined as a hybrid framework to select the most critical features in the dataset for bitcoin value prediction. The extracted features from the hybrid model are given as input to the convolutional neural network to predict the price of the bitcoins. As per the experimental results, the proposed hybrid algorithm produces better results when compared with the stand-alone version of grasshopper and neural network algorithms.


[1] D. Abraham, D. Higdon, J. Nelson and J. Ibarra, Cryptocurrency price prediction using tweet volumes and
sentiment analysis, SMU Data Sci. Rev. 1 (2018), no. 3, 1–21.
[2] J. Almeida, S. Tata, A. Moser and V. Smit, Bitcoin prediciton using ANN, Neural Networks 7 (2015), 1–12.
[3] L. Chen, C.Z. Sun, C. Li and W. Sun, Bitcoin price prediction using machine learning: an approach to sample
dimension engineering, J. Comput. Appl. Math. 365 (2020), no. 1, 112395.
[4] M. Dorigo and T. St¨utzle, Ant colony optimization, The MIT Prese, Cambridge, 2004.
[5] H. Hichem, M. Elkamel, M. Rafik, M. Toufik Mesaaoud and C. Ouahiba, A new binary grasshopper optimization
algorithm for feature selection problem, J. King Saud Univ.-Comput. Inf. Sci. 34 (2022), no. 2, 316–328.
[6] J.Z. Huang, W. Huang and J. Ni, Predicting bitcoin returns using high-dimensional technical indicators, J. Finance
Data Sci. 5 (2018), no. 3, 140–155.
[7] M. Iwamura, Y. Kitamura, T. Matsumoto and K. Saito, Can we stabilize the price of a Cryptocurrency: understanding the design of Bitcoin and its potential to compete with central Bank money, Hitotsub. J. Econ. 60 (2019),
no.1, 41–60.[8] G. Kou, X. Chao, Y. Peng, F.E. Alsaadi and E. Herrera-Viedma, Machine learning methods for systemic risk
analysis in financial sectors, Technol. Econ. Dev. Eco. 25 (2019), no. 5, 716–742.
[9] P. Lamothe-Fern´andez, D. Alaminos, P. Lamothe-L´opez and M. Fern´andez-G´amez, Deep learning methods for
modeling bitcoin price, Math. 8 (2020), no. 8, 1245.
[10] Y. Li and W. Dai, Bitcoin price forecasting method based on CNN-LSTM hybrid neural network model, J. Engin.
2020 (2020), no. 13, 344–347.
[11] M. Mafarja, I. Aljarah, H. Faris, A.I. Hammouri, A.-Z. Ala’M and S. Mirjalili, Binary grasshopper optimisation
algorithm approaches for feature selection problems, Expert Syst. Appl. 117 (2019), 267–286.
[12] M.F. Mohammadi Jalali and H. Heidari, Predicting changes in Bitcoin price using grey system theory, Financ
Innov. 6 (2020), no. 1, 1–12.
[13] M. Mudassir, S. Bennbaia, D. Unal and M. Hammoudeh, Time-series forecasting of Bitcoin prices
using high-dimensional features: a machine learning approach, Neural Comput. Appl. 2020 (2020).
[14] J. Patel, S. Shah, P. Thakkar and K. Kotecha, Predicting stock market index using fusion of machine learning
techniques, Expert Syst. Appl. 42 (2015), no. 4, 2162–2172.
[15] Patrick Jaquart, David Dann, Christof Weinhardt, Short-term bitcoin market prediction via machine learning, J.
Finance Data Sci. 7 (2021), 45–66.
[16] S.M. Raju and A.M. Tarif, Real-time prediction of BITCOIN price using machine learning techniques and public
sentiment analysis, Statist. Finance (2020). https://arxiv.org/abs/2006.14473v1.
[17] S.S. Sathe, S.M. Purandare, P.D. Pujari and S.D. Sawant, Share market prediction using artificial neural network,
Int. Educ. Res. J. 2 (2016), no. 3, 74-–75.
[18] Satoshi N, Bitcoin: A peer-to-peer electronic cash system, bitcoin.org (2008) 1–9.
[19] L. Vaddi, V. Neelisetty, B. Chowdary Vallabhaneni and K. Bhanu Prakash, Predicting crypto currency prices
using machine learning and deep learning techniques, Int. Jo. Adv. Trends Comput. Sci. Engin. 9 (2020), no. 4,
[20] Zheng, Z, Xie, S, Dai, H, Chen, X and Wang, H, An overview of blockchain technology: Architecture, consensus,
and future trends. In Proc. 2017 IEEE Int. Cong. Big Data (Big Data Congress), 2017, p. 557–564.
Volume 13, Special Issue for selected papers of ICDACT-2021
The link to the conference website is https://vitbhopal.ac.in/event/icdact_dec_21/
March 2022
Pages 53-60
  • Receive Date: 13 August 2021
  • Revise Date: 24 December 2021
  • Accept Date: 07 January 2022
  • First Publish Date: 01 March 2022