Magnetic Optimization Algorithm for training Multi Layer Perceptron

Mirjalili, Seyedali; Sadiq, Ali Safaa

doi:10.1109/iccsn.2011.6014845

Cited by 44 publications

(36 citation statements)

References 9 publications

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“…The Table 4 The classification through GWO produces the result indicated in Figure 3 and the classification rate through the algorithm is 100, which indicates that the proposed stock data is good for prediction [19,20]. The six years of daily stock price for companies from NYSE, Nasdaq, Bursa Malaysia and DSE has been provided to predict the High Price through Non Linear Autoregressive Exogenous neural network model which categorizes the data into Training, Validation and Test Set as indicated in Table 5.…”

Section: Classification Results Through Gwomentioning

confidence: 99%

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

Das

Sadiq

Mirjalili

et al. 2017

J. Phys.: Conf. Ser.

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Abstract. Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

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Section: Classification Results Through Gwomentioning

confidence: 99%

Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

Das

Sadiq

Mirjalili

et al. 2017

J. Phys.: Conf. Ser.

Self Cite

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“…ANN training was also approached using the population-based algorithms which are not strictly nature-inspired, such as magnetic optimization algorithm [143], chemical reaction optimization [166], and artificial photosynthesis and phototropism [123].…”

Section: Hybrid Ann+simentioning

confidence: 99%

Adaptation and Hybridization in Nature-Inspired Algorithms

Fister

Strnad

Yang³

2015

Adaptation, Learning, and Optimization

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Abstract. The aim of this chapter is to familiarize readers with the basics of adaptation and hybridization in nature-inspired algorithms as necessary for understanding the main contents of this book. Adaptation is a metaphor for flexible autonomous systems that respond to external changing factors (mostly environmental) by adapting their wellestablished behavior. Adaptation emerges in practically all areas of human activities as well. Such adaptation mechanisms can be used as a general problem-solving approach, though it may suffer from a lack of problem-specific knowledge. To solve specific problems with additional improvements of possible performance, hybridization can be used in order to incorporate a problem-specific knowledge from a problem domain. In order to discuss relevant issues as general as possible, the classification of problems is identified at first. Additionally, we focus on the biological foundations of adaptation that constitute the basis for the formulation of nature-inspired algorithms. This book highlights three types of inspirations from nature: the human brain, Darwinian natural selection, and the behavior of social living insects (e.g., ants, bees, etc.) and animals (e.g., swarm of birds, shoals of fish, etc.), which influence the development of artificial neural networks. evolutionary algorithms, and swarm intelligence, respectively. The mentioned algorithms that can be placed under the umbrella of computational intelligence are described from the viewpoint of adaptation and hybridization so as to show that these mechanisms are simple to develop and yet very efficient. Finally, a brief review of recent developed applications is presented.

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“…In [24], a basic version of this paradigm of interaction that accounted only for long range force of attraction, without the repulsion, was successfully applied to 14 numeric benchmark problems. Since proposed, the algorithm has been used on some applications including training a multi-layer perceptron training [25] and a traveling salesman problem [26]. A binary version of the algorithm is also proposed in [27].…”

Section: Introductionmentioning

confidence: 99%