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Dragonfly Algorithm and Its Applications in Applied Science Survey

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2019)

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE

卷 2019, 期 -, 页码 -

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HINDAWI LTD

DOI: 10.1155/2019/9293617

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Mathematical & Computational Biology Neurosciences

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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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Mixed reality in surgical telepresence: a novel extended mean value cloning with automatic trimap generation and accurate alpha matting for visualization

Roshan Dallakoti, Abeer Alsadoon, P. W. C. Prasad, Sarmad Al Aloussi, Tarik A. Rashid, Omar Hisham Alsadoon, Ahmad Alrubaie, Sami Haddad

Summary: The aim of this research is to propose an extended mean value cloning algorithm with automatic trimap generation and accurate alpha matting. This implementation improves the visualization accuracy of the merged video by reducing the discolored and smudging artefacts of the remote surgeon's boundary. It also makes the merge robust for the illumination changes by taking less processing time in real time surgery.

MULTIMEDIA TOOLS AND APPLICATIONS (2023)

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Article Multidisciplinary Sciences

Multi-population Black Hole Algorithm for the problem of data clustering

Sinan A. Salih, AbdulRahman Alsewari, H. A. S. Wahab, Mustafa K. A. Mohammed, Tarik Rashid, Debashish Das, Shadi Basurra

Summary: Data clustering is a data mining technique that classifies similar objects into groups based on their similarities. The Black Hole Algorithm (BHA) is a nature-based optimization algorithm that has shown better performance than other algorithms in solving optimization problems. This paper proposes a multi-population version of BHA called MBHA, which achieves improved performance by generating a set of best solutions. Experimental results show that MBHA outperforms BHA and comparable algorithms in terms of accuracy and robustness, and it is suitable for data clustering problems.

PLOS ONE (2023)

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