Results 11 to 20 of about 2,306,141 (191)
A Survey on Dragonfly Algorithm and its Applications in Engineering [PDF]
Evolutionary Intelligence, 2020 Dragonfly algorithm (DA) is one of the most recently developed heuristic optimization algorithms by Mirjalili in 2016. It is now one of the most widely used algorithms. In some cases, it outperforms the most popular algorithms. However, this algorithm is not far from obstacles when it comes to complex optimization problems. In this work, along with the
Chnoor M. Rahman +5 more
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A Hybrid Dragonfly Algorithm for Efficiency Optimization of Induction Motors. [PDF]
Sensors (Basel), 2022 Induction motors tend to have better efficiency on rated conditions, but at partial load conditions, when these motors operate on rated flux, they exhibit lower efficiency. In such conditions, when these motors operate for a long duration, a lot of electricity gets consumed by the motors, due to which the computational cost as well as the total running
Shukla NK, Srivastava R, Mirjalili S.
europepmc +6 more sources
Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection. [PDF]
SN Comput Sci, 2021 There are various fields are affected by the growth of data dimensionality. The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance.
Chantar H +3 more
europepmc +7 more sources
Four Algorithms on the Swapped Dragonfly [PDF]
CoRR, 2022 The Swapped Dragonfly with M routers per group and K global ports per router is denoted D3(K;M) [1]. It has n=KMM routers and is a partially populated Dragonfly. A Swapped Dragonfly with K and M restricted is studied in this paper. There are four cases.
Draper, Richard
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Binary Dragonfly Algorithm for Feature Selection [PDF]
2017 International Conference on New Trends in Computing Sciences (ICTCS), 2017 Wrapper feature selection methods aim to reduce the number of features from the original feature set to and improve the classification accuracy simultaneously. In this paper, a wrapper-feature selection algorithm based on the binary dragonfly algorithm is proposed. Dragonfly algorithm is a recent swarm intelligence algorithm that mimics the behavior of
Majdi M. Mafarja +4 more
openaire +3 more sources
The Gaussian Mutational Barebone Dragonfly Algorithm: From Design to Analysis [PDF]
Symmetry, 2022 The dragonfly algorithm is a swarm intelligence optimization algorithm based on simulating the swarming behavior of dragonfly individuals. An efficient algorithm must have a symmetry of information between the participating entities. An improved dragonfly algorithm is proposed in this paper to further improve the global searching ability and the ...
Li Yuan +3 more
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Digital Watermarking using Dragonfly Optimization Algorithm [PDF]
Journal of Information Technology Management, 2020 In this paper a novel digital watermarking algorithm is proposed. The proposed method comprises of a watermarking embedding and extraction algorithm using bio inspired optimization technique.
Satender Sharma +3 more
doaj +2 more sources
A Hybrid Improved Dragonfly Algorithm for Feature Selection [PDF]
IEEE Access, 2020 Feature selection, which eliminates irrelevant and redundant features, is one of the most efficient classification methods. However, searching for an optimal subset from the original set is still a challenging problem. This paper proposes a novel feature
Xueting Cui +4 more
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Multi-Threshold Image Segmentation Based on the Improved Dragonfly Algorithm
MathematicsIn view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image ...
Yuxue Dong, Mengxia Li, Mengxiang Zhou
doaj +2 more sources
Remote Sensing, 2023 Feature selection is a typical multiobjective problem including two conflicting objectives. In classification, feature selection aims to improve or maintain classification accuracy while reducing the number of selected features. In practical applications,
Yilin Chen +6 more
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