Vortex search optimization algorithm for training of feed-forward neural network

Sağ, Tahir; Jalil, Zainab Abdullah Jalil

doi:10.1007/s13042-020-01252-x

Cited by 14 publications

(4 citation statements)

References 37 publications

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“…However, it necessitates a large amount of training data as well as computational resources [61]. The feed-forward neural network is the most frequently used neural network architecture in ML [62]. This network is made up of three levels: an input layer, one or more hidden layers, and an output layer.…”

Section: Methodsmentioning

confidence: 99%

“…The network may have tens or hundreds of layers and may include extra components such as convolutional layers, pooling layers, and recurrent layers [65]. Backpropagation, a gradient-based optimization method, is The feed-forward neural network is the most frequently used neural network architecture in ML [62]. This network is made up of three levels: an input layer, one or more hidden layers, and an output layer.…”

Section: Methodsmentioning

confidence: 99%

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The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

Kumar,

Azamathulla,

Sharma

et al. 2023

Sustainability

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“…Aljarah, et al trained neural network using the bird swarm algorithm (BSA) algorithm in 2019 [ 72 ]. In 2021 Sağ et alused vortex search optimization algorithm for training feed-forward neural network [ 73 ].In 2022 Chatterjee et al proposed a Chaotic oppositional-based whale optimization to train a feed forward neural network [ 74 ]. In 2022 Gülcü [ 75 ] trained feed-forward neural networks using dragonfly algorithm.…”

Section: Related Workmentioning

confidence: 99%

A modified weighted chimp optimization algorithm for training feed-forward neural network

Atta

Ali²,

Elshamy³

2023

PLoS ONE

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Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.

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“…As a result, a fair balance of explorative and exploitative search behavior is achieved. We have realized that VSA has less attraction to many areas of optimization, even though it was recently accepted in solving neural network problem [283], but its yet to be adopted in tway testing.…”

Section: Physic-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Vortex search optimization algorithm for training of feed-forward neural network

Cited by 14 publications

References 37 publications

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management

A modified weighted chimp optimization algorithm for training feed-forward neural network

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

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