A hybrid dragonfly algorithm with extreme learning machine for prediction

Salam, Mustafa Abdul; Zawbaa, Hossam M.; Emary, Eid; Ghany, Kareem Kamal A.; Pârv, Bazil

doi:10.1109/inista.2016.7571839

Cited by 31 publications

(14 citation statements)

References 16 publications

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“…In [29], DA was combined with an extreme learning machine (ELM) to overcome the problems in gradient-based algorithms. In this technique to optimally select the biases of the hidden layer, DA was used.…”

Section: Hybridization Versions 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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Section: Hybridization Versions 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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“…Dragonflies are luxurious insects. There are nearly 3000 different species of these insects worldwide [28] [30] [31] [32]. The main inspiration of the DA algorithm comes from clustered behavior in static and dynamic manners.…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

Integrated Procurement-Production Inventory Model with Two-Stage Production

Utama

Kholik

Mulya

2020

JTI

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The inventory-production system concerns the effective management of the goods flows from raw materials to finished products. The Integrated Procurement-Production (IPP) system consists of many elements that must be managed effectively. The problem will be more complex if it involves deciding on the number of delivery frequencies at the retailer level. In this case, the Integrated Procurement-Production's objective function depends on the frequency of raw material shipments, the frequency of delivery of finished products, and the time of the production cycle. This study aims to develop an IPP system to maximize total profit. The decision variables used are the frequency of raw material delivery, the frequency of delivery of finished products, and the production cycle time. This study proposes the Dragonfly Algorithm (DA) as an algorithm for problem-solving. Dragonfly Algorithm is used to find the best inventory decision variables. This study conducted experiments with various iteration parameters and DA population. The results showed that the greater the iteration and the population used, the greater the profit. A sensitivity analysis of decision variables is also presented in this investigation.

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“…[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 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%

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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A hybrid dragonfly algorithm with extreme learning machine for prediction

Cited by 31 publications

References 16 publications

Dragonfly Algorithm and Its Applications in Applied Science Survey

Dragonfly Algorithm and Its Applications in Applied Science Survey

Integrated Procurement-Production Inventory Model with Two-Stage Production

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

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