Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out

Jafari, Mohammad; Chaleshtari, Mohammad Hossein Bayati

doi:10.1016/j.euromechsol.2017.06.003

Cited by 120 publications

(61 citation statements)

References 17 publications

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“…Reference [65] optimized the parameters in the analysing stress of perforated orthotropic plates. In this work, the DA utilized to reach the smallest stress value around the quasi-triangular cutout in an infinite orthotropic plate.…”

Section: Optimal Parametersmentioning

confidence: 99%

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OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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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 strengths of the algorithm in solving real-world optimization problems, the weakness of the algorithm to optimize complex optimization problems is addressed. This survey presents a comprehensive investigation of DA in the engineering area. First, an overview of the algorithm is discussed. Additionally, the different variants of the algorithm are addressed too. The combined versions of the DA with other techniques and the modifications that have been done to make the algorithm work better are shown. Besides, a survey on applications in engineering area that used DA is offered. The algorithm is compared with some other metaheuristic algorithms to demonstrate its ability to optimize problems comparing to the others. The results of the algorithm from the works that utilized the DA in the literature and the results of the benchmark functions showed that in comparison with some other algorithms DA has an excellent performance, especially for small to medium problems. Moreover, the bottlenecks of the algorithm and some future trends are discussed. Authors conduct this research with the hope of offering beneficial information about the DA to the researchers who want to study the algorithm and utilize it to optimize engineering problems.

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Section: Optimal Parametersmentioning

confidence: 99%

“…Therefore, knowing useful parameters to reduce stress concentration in various structures is crucial. In reference [45], DA used to optimize the involved parameters in analysing stress of the perforated orthotropic plates.…”

mentioning

confidence: 99%

OUP accepted manuscript

2020

Journal of Computational Design and Engineering

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“…DA [39] Constant β = 0.5 PSO [23] Learning factors c 1 = c 2 = 2, Maximum velocity = 25.5 SCA [41] Controlling parameter r 1 ∈ [0, 2] BA [25] Loudness = 0.25; Factor updating pulse emission rate γ = 0.95 HSO [42] PAR (Pitch Adjustment Rate) = 0.3 HMCR (Harmony Memory Considering Rate) = 0.95 ALO [43] controlling parameter c 1 ∈ [0, 2] SSA [44] Constant ω = [2.6] All the algorithms are developed by using "Matlab 2014b" and implemented on "Windows 10-64bit" environment on a computer having Pentium(R) Dual core T4500 @ 2.30 GHz and 2 GB of memory.…”

Section: Algorithm Parameters Settingmentioning

confidence: 99%

“…It is evident that color images contain more information compared with common images, highlighting the difficulty of satellite image segmentation. Furthermore, there are some drawbacks of the standard DA algorithm mentioned as follows: premature convergence, unbalanced exploration -exploitation [38][39][40]. In order to enhance the performance of traditional DA algorithm to a certain extent, as well as provide an efficient method to solve the problems in multilevel thresholding image segmentation, a modified dragonfly algorithm combined with opposition-based learning (OBLDA) is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the proposed method is evaluated using five satellite images and five natural images. Meanwhile, several state-of-the-art meta-heuristic algorithms are selected for comparison, such as DA [39], PSO [23], the sine cosine algorithm (SCA) [41], the BA [25], the harmony search algorithm (HSO) [42], ant lion optimization (ALO) [43], and the salp swarm algorithm (SSA) [44]. Furthermore, some indicators such as the peak signal to noise ratio (PSNR), the feature similarity index (FSIM), the structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are chosen as quality metrics to compare the performance of proposed algorithm with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

2019

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Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

Unnisa

Tatineni

Fatima

et al. 2023

Int J Communication

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Summary Wireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

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Using dragonfly algorithm for optimization of orthotropic infinite plates with a quasi-triangular cut-out

Cited by 120 publications

References 17 publications

OUP accepted manuscript

OUP accepted manuscript

Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

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