Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures

Kaveh, A.; Kamalinejad, Mohammad; Hamedani, Kiarash Biabani

doi:10.1016/j.istruc.2020.12.032

Cited by 14 publications

(4 citation statements)

References 34 publications

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“…On the other hand, some assumptions are required to be able to use the BA algorithm in the optimization process, as in the other algorithm types [33,35]. These assumptions are as below:…”

Section: Bat Algorithm (Ba)mentioning

confidence: 99%

Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification

2021

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Truss structures are one of the major civil engineering members studied in the optimization research area. In this area, various optimization applications such as topology, size, cost, weight, material usage, etc., can be conducted for different truss structure types. In this scope with the present study, various optimization processes were carried out concerning two different large-scale space trusses to minimize the structural weight. According to this state, three structural models provided via two different truss structures, including 25 bar and 72 bar truss models, were handled for evaluation of six different metaheuristics together with the modification of Lèvy flight for three of the algorithms using swarm intelligence by considering both constant and variable populations, and different ranges for iterations, too. Additionally, the effects of the Lèvy flight function and whether it is successful or not in terms of the target of optimization were also investigated by comparing with some documented studies. In this regard, some statistical calculations were also realized to evaluate the optimization method performance and detection of optimum values for any data stably and successfully. According to the results, the Jaya algorithm can handle the optimization process successfully, including the case, without grouping truss members. The positive effect of Lèvy flight on swarm-based algorithms can be seen especially for the gray wolf algorithm.

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“…On the other hand, some assumptions are required to be able to use the BA algorithm in the optimization process, as in the other algorithm types [33,35]. These assumptions are as below:…”

Section: Bat Algorithm (Ba)mentioning

confidence: 99%

Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification

2021

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“…The main contributions of this article are summarized below: A pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images is proposed for categorizing the mammogram breast cancer imageries as malignant, benign, and normal with high accuracy by reducing computational complexity. The breast mammography recognition strategy composed of five phases toward the optimized value of the classifier parameters by selecting the most relevant features from the reduced feature vector simultaneously. The breast cancer imageries are gathered via MAMMOSET dataset. The imageries are preprocessed and segmented by Tsalli's entropy 26 based multilevel 3D Otsu 27 thresholding panoptic image segmentation technique for removing noises and to locate the tumor in mammograms and to select the fine grained images. The computational complexity of multilevel threshold rapidly rises with enhancing count of thresholds. Metaheuristic approaches are typically utilized to optimize the threshold searching procedure to lessen the computational complexity involving multilevel thresholding. For reducing the computational complexities from the multilevel thresholding‐based image segmentation process, the parameters of the hybrid TE‐3D‐Otsu are optimized using the improved shuffled shepherd optimization algorithm (SSOA) 28 for selecting the fine grained regions from the input imageries. The features are extracted utilizing moment invariant wavelet feature extraction (MI‐WFE) 29 technique for identifying the important and distinct elements from the input images by extracting shape texture features. To extract shape texture features from the input imageries, the feature extraction process includes two phases: (i) discrete wavelet transform (DWT) and (ii) moment's invariants, that is, after 2 level DWT of clinical imagery, seven bands of texture features are extracted from wavelet coefficients and then apply seven moments invariant for each band. Then the images are classified with the help of pulse coupled neural network (PCNN) 30 Then the PCNN mass parameters are optimizes utilizing chaotic‐GWOA 31 for categorizing the mammogram breast cancer imageries as malignant, benign, and usual. The proposed approach is implemented in MATLAB.…”

Section: Introductionmentioning

confidence: 99%

“…• For reducing the computational complexities from the multilevel thresholding-based image segmentation process, the parameters of the hybrid TE-3D-Otsu are optimized using the improved shuffled shepherd optimization algorithm (SSOA) 28 for selecting the fine grained regions from the input imageries.…”

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confidence: 99%

Pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images

Thanupillai¹,

Basha²

2022

Concurrency and Computation

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Summary Breast cancer is a very hazardous disease that mainly affects women and leads to a high mortality rate. Early detection of this disease only can reduce the mortality rate. Previously, several methods were used to detect this cancer, but none of them provides sufficient accuracy. To deal this issue, a pulse coupled neural network optimized with chaotic grey wolf algorithm is proposed in this article for the classification of breast cancer using mammogram images. The breast cancer images are gathered from MAMMOSET dataset. The images are preprocessed and segmented with the help of Tsalli's entropy based multilevel 3D Otsu (TE‐3D‐Otsu) thresholding method. Then the parameters of the TE‐3D‐Otsu are optimized using the improved shuffled shepherd optimization algorithm. The features are extracted using moment invariant wavelet feature extraction technique. The images are classified using pulse coupled neural network (PCNN). Then, PCNN mass parameters are optimizes utilizing chaotic grey wolf optimization algorithm (chaotic‐GWOA) for categorizing the mammogram breast cancer imageries, such as malignant, benign, and normal. The proposed method is activated on MATLAB, its performance is analyzed with existing methods. The simulation results show that the proposed BC‐PCNN‐CGWOA attains the accuracy 57.86%, 85.94%, and 53.45% is greater than the existing methods.

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“…A broad variety of BA variants have been developed in order to address this problem and thus enhance the performance of the standard BA. These BA variants along with the standard BA have been applied in various research fields [7,[71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88].…”

Section: Introductionmentioning

confidence: 99%

Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system

Dong

Mugemanyi

et al. 2022

IET Renewable Power Gen

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Here, a novel Gaussian bare-bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare-bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14-bus, IEEE 57-bus and IEEE 118-bus. Simulation results show that GBBBA and DeGBBBA are robust and effective in solving the ORPD problem.

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Enhanced versions of the shuffled shepherd optimization algorithm for the optimal design of skeletal structures

Cited by 14 publications

References 34 publications

Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification

Evaluation of Metaheuristic-Based Methods for Optimization of Truss Structures via Various Algorithms and Lèvy Flight Modification

Pulse coupled neural network optimized with chaotic grey wolf algorithm for breast cancer classification using mammogram images

Dynamic exploitation Gaussian bare‐bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system

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