Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization

Knowledge-Based Systems, Год журнала: 2022, Номер 260, С. 110146 - 110146

Опубликована: Ноя. 29, 2022

Язык: Английский

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Improved bald eagle search algorithm for global optimization and feature selection

Alexandria Engineering Journal, Год журнала: 2023, Номер 68, С. 141 - 180

Опубликована: Янв. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Язык: Английский

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A Review on Nature-Inspired Algorithms for Cancer Disease Prediction and Classification

Mathematics, Год журнала: 2023, Номер 11(5), С. 1081 - 1081

Опубликована: Фев. 21, 2023

In the era of healthcare and its related research fields, dimensionality problem high-dimensional data is a massive challenge as it crucial to identify significant genes while conducting on diseases like cancer. As result, studying new Machine Learning (ML) techniques for raw gene expression biomedical an important field research. Disease detection, sample classification, early disease prediction are all analyses in bioinformatics. Recently, machine-learning have dramatically improved analysis high-dimension sets. Nonetheless, researchers’ studies faced vast dimensions, i.e., features (genes) with very low space. this paper, two-dimensionality reduction methods, feature selection, extraction introduced systematic comparison several dimension data. We presented review some most popular nature-inspired algorithms analyzed them. The paper mainly focused original principles behind each their applications cancer classification from Lastly, advantages disadvantages evaluated. This may guide researchers choose effective algorithm satisfactory

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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BEESO: Multi-strategy Boosted Snake-Inspired Optimizer for Engineering Applications

Journal of Bionic Engineering, Год журнала: 2023, Номер 20(4), С. 1791 - 1827

Опубликована: Янв. 31, 2023

Язык: Английский

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Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete

Developments in the Built Environment, Год журнала: 2023, Номер 17, С. 100307 - 100307

Опубликована: Дек. 22, 2023

In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower costs, speedy 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving optimum mixture composition 3DPFRC remains a daunting task, entailing consideration of multiple variables necessitating an extensive trial-and-error experimental process. Therefore, this study investigated application different metaheuristic optimization algorithms predict compressive strength (CS) 3DPFRC. A database 299 data samples with 16 input features was compiled from studies in literature. Six algorithms, such as human felicity algorithm (HFA), differential evolution (DEA), nuclear reaction (NRO), Harris hawks (HHO), lightning search (LSA), tunicate swarm (TSA) were applied identify optimal hyperparameter combination random forest (RF) model predicting CS Different statistical metrics 10-fold cross-validation used evaluate accuracy models. The TSA-RF exhibited superior performance compared other models, correlation (R), mean absolute error (MAE), root square (RMSE) values 0.99, 2.10 MPa, 3.59 respectively. LSA-RF also performed well, R, MAE, RMSE 2.93 6.23 SHapley Additive exPlanation (SHAP) interpretability elucidates intricate relationships between their effects on CS, thereby offering invaluable insights performance-based mix proportion design

Язык: Английский

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A novel Q-learning-based hybrid algorithm for the optimal offloading and scheduling in mobile edge computing environments

Journal of Network and Computer Applications, Год журнала: 2023, Номер 214, С. 103617 - 103617

Опубликована: Март 2, 2023

Язык: Английский

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Enhancing grasshopper optimization algorithm (GOA) with levy flight for engineering applications

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Янв. 4, 2023

The grasshopper optimization algorithm (GOA) is a meta-heuristic proposed in 2017 mimics the biological behavior of swarms seeking food sources nature for solving problems. Nonetheless, some shortcomings exist origin GOA, and GOA global search ability more or less insufficient precision also needs to be further improved. Although there are many different variants literature, problem inefficient rough has still emerged variants. Aiming at these deficiencies, this paper develops an improved version with Levy Flight mechanism called LFGOA alleviate GOA. achieved suitable balance between exploitation exploration during searching most promising region. performance tested using 23 mathematical benchmark functions comparison eight well-known algorithms seven real-world engineering statistical analysis experimental results show efficiency LFGOA. According obtained results, it possible say that can potential alternative solution problems as high capabilities.

Язык: Английский

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An enhanced chameleon swarm algorithm for global optimization and multi-level thresholding medical image segmentation

Neural Computing and Applications, Год журнала: 2024, Номер 36(15), С. 8775 - 8823

Опубликована: Март 5, 2024

Язык: Английский

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Enhanced Aquila optimizer algorithm for global optimization and constrained engineering problems

Mathematical Biosciences & Engineering, Год журнала: 2022, Номер 19(12), С. 14173 - 14211

Опубликована: Янв. 1, 2022

<abstract><p>The Aquila optimizer (AO) is a recently developed swarm algorithm that simulates the hunting behavior of birds. In complex optimization problems, an AO may have slow convergence or fall in sub-optimal regions, especially high ones. This paper tries to overcome these problems by using three different strategies: restart strategy, opposition-based learning and chaotic local search. The named as mAO was tested 29 CEC 2017 functions five engineering constrained problems. results prove superiority efficiency solving many issues.</p></abstract>

Язык: Английский

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