Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction

Yuliyono, Andary Dadang; Girsang, Abba Suganda

doi:10.25046/aj040549

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…Hence, an optimized LSTM with ABC to forecast the bitcoin price was introduced in Yuliyono and Girsang. 22 The combination of ABC and RNN was also proposed in Bosire 23 for traffic volume forecasting. This time the results were compared with standard backpropagation models.…”

Section: Related Workmentioning

confidence: 99%

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

et al. 2020

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This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.

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Section: Related Workmentioning

confidence: 99%

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

et al. 2020

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“…In [28], the authors proposed the artificial bee colony (ABC) algorithm, which imitates the behavior of bee colonies in foraging to search for the optimal values of the hyperparameters of the LSTM topology for bitcoin price prediction. The finding of this study showed that the LSTM model evolved using the ABC outperformed handcrafted LSTM models.…”

Section: Related Workmentioning

confidence: 99%

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Bataineh

Kaur

2021

IEEE Access

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This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification. Designing optimal topologies involves determining the best configuration of hyperparameters that will give the best performance. The current state-of-the-art LSTM topologies are often designed using trial and error approaches which are incredibly time-consuming and require domain experts. Our proposed method, referred to as CSA-LSTM, is evaluated using the Large Movie Review Dataset (IMDB). Furthermore, to verify the robustness of the hyperparameters discovered by CSA for the IMDB dataset, we have used them for the other datasets, viz. the Twitter US Airline Sentiment and the SMS Spam Collection. Additionally, the discovered hyperparameters for the LSTM are combined with pre-determined convolutional neural network (CNN) layers to achieve the same or better results to fast the training time and fewer trainable parameters. For further verification and evaluation of the generalization ability and effectiveness of the proposed approach, it is compared with four machine learning algorithms widely used for text classification tasks: (1) random forest (RF), (2) logistic regression (LR), (3) support vector machine (SVM), and (4) multinomial naive Bayes (NB). The results of our experiments show that the LSTM topologies automatically designed by our CSA method are less expensive, reusable and outperform the machine learning algorithms and other models in the literature evaluated on the same three datasets. Through our proposed method, LSTM's best topology can be self-determined without any human intervention, making CSA-based algorithms a promising approach to automatically design optimal LSTM topologies that provide the best performance for a given task.INDEX TERMS clonal selection algorithm, deep learning, hyperparameters optimization, IMDB, immunocomputing, long short-term memory.

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“…The PSO has also been improved by [63] with the Adaptive Cooperative Particle Swarm Optimisation (ACPSO) proposed, which incorporates a learning automata to adaptively split the sub-population of cooperative PSO, thereby making the decision variables with strong coupling connection to enter the same sub-population. Conversely, other SI based optimisers explored for DL include the salp-swarm optimiser [64], harmony search optimiser on variational stacked autoencoders [65], whale optimisation algorithm(WOA) using bidirectional RNN [66], the Artificial Bee Colony (ABC) for optimizing hyperparameters for LSTM models [67], the AC-Parametric WOA (ACP-WOA) [68] for predicting biomedical images, symbiotic organisms search (SOS) algorithm [69], lion swarm optimiser(LSO) [70], and many others. Nevertheless, a comparison of the performance of these genetic algorithms shows that the GWO convergence rate is fastest compared to the Genetic Algorithm (GA), and PSO [71].…”

Section: Particle Swarm Optimisers (Pso)mentioning

confidence: 99%

Advances in Optimisation Algorithms and Techniques for Deep Learning

Nwankpa¹

2020

Adv. sci. technol. eng. syst. j.

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In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

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Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction

Cited by 13 publications

References 24 publications

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

Advances in Optimisation Algorithms and Techniques for Deep Learning

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