Optimal design of low- and high-rise building structures by Tribe-Harmony Search algorithm

Azizi, Mahdi; Talatahari, Siamak; Basiri, Mahla; Shishehgarkhaneh, Milad Baghalzadeh

doi:10.1016/j.dajour.2022.100067

Cited by 13 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the BC detection results of the MHODL-BCDMI approachare tested on the MIAS-mammography dataset [27]. It holds 319 samples with three class labels as defined in Table 1.…”

Section: A Simulation Environmentmentioning

confidence: 99%

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Ashwini,

Suguna,

Vadivelan

2023

IJRITCC

View full text Add to dashboard Cite

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms.

show abstract

“…In this section, the BC detection results of the MHODL-BCDMI approachare tested on the MIAS-mammography dataset [27]. It holds 319 samples with three class labels as defined in Table 1.…”

Section: A Simulation Environmentmentioning

confidence: 99%

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Ashwini,

Suguna,

Vadivelan

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…To fulfill handle this problem, one way is to introduce more efficient methods to reduce the required computational cost. There are limited works that address this challenging problem, such as [35][36][37]. This aim is covered in this paper by presenting Developed Swarm Optimizer (DSO) [10] and Feasible-based mechanism as advanced methods.…”

Section: Introductionmentioning

confidence: 99%

A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry

Nouhi

Jahani

Talatahari

et al. 2022

Decision Analytics Journal

View full text Add to dashboard Cite

An enhanced guided stochastic search with repair deceleration mechanism for very high-dimensional optimization problems of steel double-layer grids

Azad,

Aminbakhsh,

Gandomi

2024

Struct Multidisc Optim

View full text Add to dashboard Cite

Finding reasonably good solutions using a fewer number of objective function evaluations has long been recognized as a good attribute of an optimization algorithm. This becomes more important, especially when dealing with very high-dimensional optimization problems, since contemporary algorithms often need a high number of iterations to converge. Furthermore, the excessive computational effort required to handle the large number of design variables involved in the optimization of large-scale steel double-layer grids with complex configurations is perceived as the main challenge for contemporary structural optimization techniques. This paper aims to enhance the convergence properties of the standard guided stochastic search (GSS) algorithm to handle computationally expensive and very high-dimensional optimization problems of steel double-layer grids. To this end, a repair deceleration mechanism (RDM) is proposed, and its efficiency is evaluated through challenging test examples of steel double-layer grids. First, parameter tuning based on rigorous analyses of two preliminary test instances is performed. Next, the usefulness of the proposed RDM is further investigated through two very high-dimensional instances of steel double-layer grids, namely a 21,212-member free-form double-layer grid, and a 25,514-member double-layer multi-dome, with 21,212 and 25,514 design variables, respectively. The obtained numerical results indicate that the proposed RDM can significantly enhance the convergence rate of the GSS algorithm, rendering it an efficient tool to handle very high-dimensional sizing optimization problems.

show abstract

Optimal design of low- and high-rise building structures by Tribe-Harmony Search algorithm

Cited by 13 publications

References 54 publications

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

A swarm optimizer with modified feasible-based mechanism for optimum structure in steel industry

An enhanced guided stochastic search with repair deceleration mechanism for very high-dimensional optimization problems of steel double-layer grids

Contact Info

Product

Resources

About