A Comparative Study of CSO and PSO Trained Artificial Neural Network for Stock Market Prediction

Chittineni, Suresh; Mounica, Vabbilisetty; Abhilash, Kaligotla; Satapathy, Suresh Chandra; Reddy, P. Jayarama

doi:10.1007/978-3-642-24043-0_20

Cited by 5 publications

(4 citation statements)

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“…27 These nature-inspired optimization algorithms are also extensively used for training ANNs. Some examples are follows: ChSO and PSO algorithms are used for training neural networks for predicting stock market 28 ; ABC optimization algorithm is used for training ANNs for the application of pattern classification 29 ; cat swarm optimization is used to train an ANN for the problem of classifying six distinct data sets. 30 From the above discussions, the nature-inspired optimization algorithms are extensively used for solving numerical optimization, selecting significant features, and for training the ANNs.…”

Section: Introductionmentioning

confidence: 99%

A novel improved crow‐search algorithm to classify the severity in digital mammograms

Chakravarthy

Rajaguru

2020

Int J Imaging Syst Tech

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The survival rates of breast cancer are going up due to the emerging increase in its screening and diagnosis methods. However, breast cancer is yet the most intrusive disease found in women. Many techniques are emerging during recent years for the investigation of breast cancer using imaging modalities. The paper intends to categorize the severity present in the digital mammography images as either benign (B) or malignant (M) using an improved crow‐search optimization algorithm (ImCSOA). In the literature, the CSOA is generally used for solving several feature selection and numerical optimization problems. The objective is to utilize this popular optimization algorithm for the problem of biomedical image classification. However, if this algorithm is applied directly to classification problems, then it will result in poor classification of data. Hence, the original CSO (OCSO) algorithm undergoes suitable enhancements using a novel controlled parameter tuning, control operator and chaotic‐maps‐based controlled randomness. Four distinct chaotic maps are used for controlling the randomness in the OCSO algorithm. The mammogram images are obtained from the Mammographic Image Analysis Society and Digital Database for Screening Mammography data sets for the evaluation. The classification is accomplished through discrete wavelet transform‐based statistical features that are extracted at two levels [level 4 (L4) and level 6 (L6)] of decomposition. For both data sets, the ImCSOA with L4 and L6 decomposed bior4.4 wavelet features provides the maximum accuracy of around 85% to 86%, which is approximately 62% to 88% better than the OCSO algorithm with L4 and L6 decomposed bior4.4 wavelet features.

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Section: Introductionmentioning

confidence: 99%

A novel improved crow‐search algorithm to classify the severity in digital mammograms

Chakravarthy

Rajaguru

2020

Int J Imaging Syst Tech

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“…Chittineni et al used CSO and PSOalgorithms to train ANN and then applied their method on stock market prediction. eir comparison results showed that CSO algorithm performed better than the PSO algorithm[44].…”

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confidence: 98%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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“….7 CSO/PSO+ANN: Chittineni et al used CSO and PSO algorithms to train ANN and then applied their method on stock market prediction. Their comparison results showed that the CSO algorithm performed better than the PSO algorithm [45]. 4.8 CS-FLANN: Kumar et al combined the CSO algorithm with Functional Link Artificial Neural Network (FLANN) to develop an evolutionary filter to remove Gaussian noise[46].…”

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confidence: 99%

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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A Comparative Study of CSO and PSO Trained Artificial Neural Network for Stock Market Prediction

Cited by 5 publications

References 1 publication

A novel improved crow‐search algorithm to classify the severity in digital mammograms

A novel improved crow‐search algorithm to classify the severity in digital mammograms

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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