Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Kumaran, J.; Sasikala, J.

doi:10.5120/ijca2017913636

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…In reference [48], DA-based artificial neural network (ANN) model was utilized to estimate India's primary fuel demand. Two multilayer feedforward networks were used.…”

Section: Applications Of Damentioning

confidence: 99%

Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman

Rashid

2019

Computational Intelligence and Neuroscience

107

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One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm's weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.

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“…In reference [48], DA-based artificial neural network (ANN) model was utilized to estimate India's primary fuel demand. Two multilayer feedforward networks were used.…”

Section: Applications Of Damentioning

confidence: 99%

Dragonfly Algorithm and Its Applications in Applied Science Survey

Rahman

Rashid

2019

Computational Intelligence and Neuroscience

107

View full text Add to dashboard Cite

show abstract

“…[25][26][27] To avoid static collision with "N" number of neighbors, separation can be calculated as in Equation (17) with X and X j being the current and jth position of the fly respectively: They exhibit two phases of movements such as static or exploration phase and dynamic or exploitation phase in which the flies form groups for hunting their prey and migrates over long distance, respectively.…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

“…The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15). The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15).…”

Section: Implementation Of Fcm-based Dementia Classificationmentioning

confidence: 99%

“…The fundamental behaviors of DA are separation, alignment, cohesion, and distraction. [25][26][27] To avoid static collision with "N" number of neighbors, separation can be calculated as in Equation (17) with X and X j being the current and jth position of the fly respectively:…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

“…The feature vector x k will be initialized as 80 statistical feature values and Euclidean distance is used to compute D ik (x k ,v i ). The membership value and cluster center are found by using Equations (26) and (27) while the objective function is calculated using Equation (15). The threshold for stopping criterion ϵ is chosen as 0.01 and the optimal value of fuzzifier (m) and a number of clusters (c) will be calculated using trial and error method as shown in Figure 19.…”

Section: Implementation Of Fcm-based Dementia Classificationmentioning

confidence: 99%

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Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly‐based hybrid algorithms

Bharanidharan¹,

Rajaguru²

2019

Int J Imaging Syst Tech

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Most often clinicians require automated computer‐aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly‐based features are used to improve the accuracy of well‐known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross‐sectional MRI of 65 non‐dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above‐mentioned three individual swarm intelligence algorithms is compared with non‐swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above‐mentioned swarm intelligence algorithms while using dragonfly‐based features and Fuzzy C means‐based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly‐based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly‐particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above‐mentioned individual classifiers yield accuracy of less than 66%.

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Review of Meta-Heuristic Optimization based Artificial Neural Networks and its Applications

Devikanniga¹,

Vetrivel²,

Badrinath³

2019

J. Phys.: Conf. Ser.

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There are several meta-heuristic optimization algorithms developed on inspiration from nature. Artificial neural network proves to be efficient among other machine learning techniques. The efficiency of classification and prediction is improved by optimizing artificial neural network using the meta-heuristic optimization algorithms. The review of some of these hybrid artificial neural networks that are applied for benchmark datasets and to specific real-time experiments for classification and prediction are discussed. Upcoming sections cover the current trending research topics dealing with optimized artificial neural network concepts and provide some interesting insights for researchers to use in their respective applications domains of interest.

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Dragonfly Optimization based ANN Model for Forecasting India's Primary Fuels' Demand

Cited by 4 publications

References 7 publications

Dragonfly Algorithm and Its Applications in Applied Science Survey

Dragonfly Algorithm and Its Applications in Applied Science Survey

Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly‐based hybrid algorithms

Review of Meta-Heuristic Optimization based Artificial Neural Networks and its Applications

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