A Literature Review and Critical Analysis of Metaheuristics Recently Developed

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 31(1), С. 125 - 146

Опубликована: Июль 22, 2023

Abstract Metaheuristic algorithms have applicability in various fields where it is necessary to solve optimization problems. It has been a common practice this field for several years propose new that take inspiration from natural and physical processes. The exponential increase of controversial issue researchers criticized. However, their efforts point out multiple issues involved these practices insufficient since the number existing metaheuristics continues yearly. To know current state problem, paper analyzes sample 111 recent studies so-called new, hybrid, or improved are proposed. Throughout document, topics reviewed will be addressed general perspective specific aspects. Among study’s findings, observed only 43% analyzed papers make some mention No Free Lunch (NFL) theorem, being significant result ignored by most presented. Of studies, 65% present an version established algorithm, which reveals trend no longer based on analogies. Additionally, compilation solutions found engineering problems commonly used verify performance state-of-the-art demonstrate with low level innovation can erroneously considered as frameworks years, known Black Widow Optimization Coral Reef analyzed. study its components they do not any innovation. Instead, just deficient mixtures different evolutionary operators. This applies extension recently proposed versions.

Язык: Английский

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Accurate multilevel thresholding image segmentation via oppositional Snake Optimization algorithm: Real cases with liver disease

Computers in Biology and Medicine, Год журнала: 2024, Номер 169, С. 107922 - 107922

Опубликована: Янв. 4, 2024

Язык: Английский

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Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants

Results in Engineering, Год журнала: 2024, Номер 23, С. 102637 - 102637

Опубликована: Июль 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Язык: Английский

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A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Archives of Computational Methods in Engineering, Год журнала: 2022, Номер 30(2), С. 765 - 797

Опубликована: Сен. 21, 2022

Язык: Английский

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Gradient-Based Optimizer (GBO): A Review, Theory, Variants, and Applications

Archives of Computational Methods in Engineering, Год журнала: 2022, Номер 30(4), С. 2431 - 2449

Опубликована: Дек. 30, 2022

Язык: Английский

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Journal of Healthcare Engineering, Год журнала: 2022, Номер 2022, С. 1 - 22

Опубликована: Окт. 22, 2022

Kidney tumor (KT) is one of the diseases that have affected our society and seventh most common in both men women worldwide. The early detection KT has significant benefits reducing death rates, producing preventive measures reduce effects, overcoming tumor. Compared to tedious time-consuming traditional diagnosis, automatic algorithms deep learning (DL) can save diagnosis time, improve test accuracy, costs, radiologist's workload. In this paper, we present models for diagnosing presence KTs computed tomography (CT) scans. Toward detecting classifying KT, proposed 2D-CNN models; three are concerning such as a 2D convolutional neural network with six layers (CNN-6), ResNet50 50 layers, VGG16 16 layers. last model classification four (CNN-4). addition, novel dataset from King Abdullah University Hospital (KAUH) been collected consists 8,400 images 120 adult patients who performed CT scans suspected kidney masses. was divided into 80% training set 20% testing set. accuracy results CNN-6 reached 97%, 96%, 60%, respectively. At same CNN-4 92%. Our achieved promising results; they enhance patient conditions high workload providing them tool automatically assess condition kidneys, risk misdiagnosis. Furthermore, increasing quality healthcare service change disease's track preserve patient's life.

Язык: Английский

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Four vector intelligent metaheuristic for data optimization

Computing, Год журнала: 2024, Номер 106(7), С. 2321 - 2359

Опубликована: Апрель 18, 2024

Язык: Английский

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Hybrid random forest models optimized by Sparrow search algorithm (SSA) and Harris hawk optimization algorithm (HHO) for slope stability prediction

Transportation Geotechnics, Год журнала: 2024, Номер 48, С. 101305 - 101305

Опубликована: Июнь 21, 2024

Язык: Английский

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River discharge prediction based multivariate climatological variables using hybridized long short-term memory with nature inspired algorithm

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132453 - 132453

Опубликована: Дек. 1, 2024

Язык: Английский

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A sustainable production-inventory model with CO2 emission, electricity and fuel consumption under quality degradation and stochastic demand: a case study in the agri-food industry

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Янв. 5, 2024

Язык: Английский

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