An Opposition-Based Great Wall Construction Metaheuristic Algorithm With Gaussian Mutation for Feature Selection DOI Creative Commons
Farouq Zitouni, Abdulaziz S. Almazyad, Guojiang Xiong

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 30796 - 30823

Опубликована: Янв. 1, 2024

The feature selection problem involves selecting a subset of relevant features to enhance the performance machine learning models, crucial for achieving model accuracy. Its complexity arises from vast search space, necessitating application metaheuristic methods efficiently identify optimal subsets. In this work, we employed recently proposed algorithm named Great Wall Construction Algorithm address challenge – powerful optimizer with promising results. To algorithm's in terms exploration, exploitation, and avoidance local optima, integrated opposition-based Gaussian mutation techniques. underwent comprehensive comparative analysis against ten influential state-of-the-art methodologies, encompassing seven contemporary algorithms three classical counterparts. evaluation covered 22 datasets varying sizes, ranging 9 856 features, included utilization six distinct metrics related accuracy, classification error rate, number selected completion time facilitate comparisons. obtained numerical results rigorous scrutiny through several non-parametric statistical tests, including Friedman test, post hoc Dunn's Wilcoxon signed ranks test. resulting mean p-values unequivocally demonstrate superior efficacy addressing problem. Matlab source code approach is available access via link "https://github.com/".

Язык: Английский

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems DOI Creative Commons
Mohammad Dehghani,

Gulnara Bektemyssova,

Zeinab Montazeri

и другие.

Biomimetics, Год журнала: 2023, Номер 8(6), С. 507 - 507

Опубликована: Окт. 23, 2023

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates natural behavior of lyrebirds in wild is introduced. The fundamental inspiration LOA strategy when faced with danger. situation, scan their surroundings carefully, then either run away or hide somewhere, immobile. theory described and mathematically modeled two phases: (i) exploration based on simulation lyrebird escape (ii) exploitation hiding strategy. performance was evaluated optimization CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. results show proposed approach has high ability terms exploration, exploitation, balancing them during search process problem-solving space. order evaluate capability dealing tasks, obtained from were compared twelve well-known algorithms. superior competitor algorithms by providing better most benchmark functions, achieving rank first best optimizer. A statistical analysis shows significant superiority comparison addition, efficiency handling real-world applications investigated through twenty-two constrained problems 2011 four engineering design problems. effective tasks while

Язык: Английский

Процитировано

45
Optimizing Task Offloading with Metaheuristic Algorithms Across Cloud, Fog, and Edge Computing Networks: A Comprehensive Survey and State-of-the-Art Schemes DOI
Amir M. Rahmani, Amir Haider,

Parisa Khoshvaght

и другие.

Sustainable Computing Informatics and Systems, Год журнала: 2025, Номер unknown, С. 101080 - 101080

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

6
A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics DOI Creative Commons
Zoran Jakšić, Swagata Devi, Olga Jakšić

и другие.

Biomimetics, Год журнала: 2023, Номер 8(3), С. 278 - 278

Опубликована: Июнь 28, 2023

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use large number areas. Novel methods advances are being published at an accelerated pace. Because that, spite the fact there lot surveys reviews they quickly become dated. Thus, it importance keep pace with current developments. In this review, we first consider possible classification bio-inspired optimization because papers dedicated area relatively scarce often contradictory. We proceed by describing some detail more prominent approaches, as well those most recently published. Finally, biomimetic two related wide fields, namely microelectronics (including circuit design optimization) nanophotonics inverse structures such photonic crystals, nanoplasmonic configurations metamaterials). attempted broad survey self-contained so can be not only scholars but also all interested latest developments attractive area.

Язык: Английский

Процитировано

41
An adaptive hybrid mutated differential evolution feature selection method for low and high-dimensional medical datasets DOI
Reham R. Mostafa,

Ahmed M. Khedr,

Zaher Al Aghbari

и другие.

Knowledge-Based Systems, Год журнала: 2023, Номер 283, С. 111218 - 111218

Опубликована: Ноя. 21, 2023

Язык: Английский

Процитировано

34
The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review DOI Creative Commons

G. Mostafa,

Hamdi A. Mahmoud,

Tarek Abd El‐Hafeez

и другие.

BMC Medical Informatics and Decision Making, Год журнала: 2024, Номер 24(1)

Опубликована: Окт. 4, 2024

Язык: Английский

Процитировано

17
Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy DOI

Damo Qian,

Keyu Liu, Shiming Zhang

и другие.

Applied Intelligence, Год журнала: 2024, Номер 54(17-18), С. 7750 - 7764

Опубликована: Июнь 13, 2024

Язык: Английский

Процитировано

15
An improved Genghis Khan optimizer based on enhanced solution quality strategy for global optimization and feature selection problems DOI
Mahmoud Abdel-Salam, Ahmed Ibrahim Alzahrani,

Fahad Alblehai

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 302, С. 112347 - 112347

Опубликована: Авг. 5, 2024

Язык: Английский

Процитировано

14
A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm DOI Creative Commons

Hanie Alirezapour,

N. Mansouri,

Behnam Mohammad Hasani Zade

и другие.

Neural Processing Letters, Год журнала: 2024, Номер 56(1)

Опубликована: Фев. 12, 2024

Abstract Recent growth in data dimensions presents challenges to mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional By removing these redundant unwanted features, the can be reduced. The feature selection process eliminates a small set relevant important features from large set, reducing size dataset. Multiple optimization problems solved using metaheuristic algorithms. Recently, Grasshopper Optimization Algorithm (GOA) has attracted attention researchers as swarm intelligence algorithm based on metaheuristics. An extensive review papers GOA-based algorithms years 2018–2023 is presented research area GOA. comparison methods presented, along with evaluation strategies simulation environments this paper. Furthermore, study summarizes classifies GOA areas. Although many have introduced their novelty problem, open enhancements remain. survey concludes discussion about some that require further attention.

Язык: Английский

Процитировано

11
V-shaped and S-shaped binary artificial protozoa optimizer (APO) algorithm for wrapper feature selection on biological data DOI Creative Commons
Amir Seyyedabbasi, Gang Hu, Hisham A. Shehadeh

и другие.

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 21, 2025

Язык: Английский

Процитировано

1
Enhancing Non-Invasive Blood Glucose Prediction from Photoplethysmography Signals via Heart Rate Variability-Based Features Selection Using Metaheuristic Algorithms DOI Creative Commons

Saifeddin Alghlayini,

Mohammed Azmi Al‐Betar, Mohamed Atef

и другие.

Algorithms, Год журнала: 2025, Номер 18(2), С. 95 - 95

Опубликована: Фев. 8, 2025

Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses non-invasive estimation BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Harris Hawks (BHHO), and Genetic (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy optimize selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) 13.17 mg/dL, root square (RMSE) 15.36 94.74% predictions falling within clinically acceptable Clarke grid (CEG) zone A, none in dangerous zones. research underscores efficiency HRV PPG for monitoring, demonstrating effectiveness integrating ML approaches enhanced diabetes monitoring.

Язык: Английский

Процитировано

1