FDLA: Fractional Dragonfly based Load balancing Algorithm in cluster cloud model

Kumar, C. Ashok; Vimala, R.; Britto, K. R. Aravind; Devi, S. S.

doi:10.1007/s10586-018-1977-6

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…Therefore, a load balancing method is required to allocate the task to the virtual machines (VMs) without influencing the system's performance. Kumar et al [54] provided a method for load balancing, named as a fractional dragonfly load balancing algorithm (FDLA). In the proposed work two selection probabilities and fractional DA were examined.…”

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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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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“…It has a high level of performance and a broad range of applications in real life. Many applications, including parameter optimization [47], feature selection [48], load balancing [49], modeling [50], and others [51], have been effectively implemented using it. Many trials with complicated, high-dimensional, and multi-modal functions, on the other hand, demonstrated that DA had some drawbacks in some situations.…”

Section: Introductionmentioning

confidence: 99%

The Gaussian Mutational Barebone Dragonfly Algorithm: From Design to Analysis

et al. 2022

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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 convergence speed of DA. The improved DA is named GGBDA, which adds Gaussian mutation and Gaussian barebone on the basis of DA. Gaussian mutation can randomly update the individual positions to avoid the algorithm falling into a local optimal solution. Gaussian barebone can quicken the convergent speed and strengthen local exploitation capacities. Enhancing algorithm efficiency relative to the symmetric concept is a critical challenge in the field of engineering design. To verify the superiorities of GGBDA, this paper sets 30 benchmark functions, which are taken from CEC2014 and 4 engineering design problems to compare GGBDA with other algorithms. The experimental result show that the Gaussian mutation and Gaussian barebone can effectively improve the performance of DA. The proposed GGBDA, similar to the DA, presents improvements in global optimization competence, search accuracy, and convergence performance.

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