Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension

Computers in Biology and Medicine, Год журнала: 2023, Номер 165, С. 107408 - 107408

Опубликована: Авг. 29, 2023

Язык: Английский

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Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection

Neurocomputing, Год журнала: 2023, Номер 551, С. 126467 - 126467

Опубликована: Июнь 21, 2023

Язык: Английский

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A hyper-heuristic algorithm via proximal policy optimization for multi-objective truss problems

Expert Systems with Applications, Год журнала: 2024, Номер 256, С. 124929 - 124929

Опубликована: Июль 30, 2024

Язык: Английский

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Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography

Computers in Biology and Medicine, Год журнала: 2024, Номер 171, С. 108038 - 108038

Опубликована: Фев. 17, 2024

Язык: Английский

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Enhancing slime mould algorithm for engineering optimization: leveraging covariance matrix adaptation and best position management

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(4), С. 151 - 183

Опубликована: Июнь 12, 2024

Abstract The slime mould algorithm (SMA), as an emerging and promising swarm intelligence algorithm, has been studied in various fields. However, SMA suffers from issues such easily getting trapped local optima slow convergence, which pose challenges when applied to practical problems. Therefore, this study proposes improved SMA, named HESMA, by incorporating the covariance matrix adaptation evolution strategy (CMA-ES) storing best position of each individual (SBP). On one hand, CMA-ES enhances algorithm’s exploration capability, addressing issue being unable explore vicinity optimal solution. other SBP convergence speed prevents it diverging inferior solutions. Finally, validate effectiveness our proposed conducted experiments on 30 IEEE CEC 2017 benchmark functions compared HESMA with 12 conventional metaheuristic algorithms. results demonstrated that indeed achieved improvements over SMA. Furthermore, highlight performance further, 13 advanced algorithms, showed outperformed these algorithms significantly. Next, five engineering optimization problems, experimental revealed exhibited significant advantages solving real-world These findings further support practicality complex design challenges.

Язык: Английский

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Gaussian mutation-alpine skiing optimization algorithm-recurrent attention unit-gated recurrent unit-extreme learning machine model: an advanced predictive model for predicting evaporation

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(5), С. 1803 - 1830

Опубликована: Янв. 25, 2024

Язык: Английский

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Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction

Computers in Biology and Medicine, Год журнала: 2024, Номер 175, С. 108437 - 108437

Опубликована: Апрель 9, 2024

Язык: Английский

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Advances in Slime Mould Algorithm: A Comprehensive Survey

Biomimetics, Год журнала: 2024, Номер 9(1), С. 31 - 31

Опубликована: Янв. 4, 2024

The slime mould algorithm (SMA) is a new swarm intelligence inspired by the oscillatory behavior of moulds during foraging. Numerous researchers have widely applied SMA and its variants in various domains field proved value conducting literatures. In this paper, comprehensive review introduced, which based on 130 articles obtained from Google Scholar between 2022 2023. study, firstly, theory described. Secondly, improved are provided categorized according to approach used apply them. Finally, we also discuss main applications SMA, such as engineering optimization, energy machine learning, network, scheduling image segmentation. This presents some research suggestions for interested algorithm, additional multi-objective discrete SMAs extending neural networks extreme learning machining.

Язык: Английский

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BGHO‐E2EB Model: Enhancing IoT Security With Gaussian Artificial Hummingbird Optimization and Blockchain Technology

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(1)

Опубликована: Янв. 1, 2025

ABSTRACT The Internet of Things (IoT) is transforming numerous sectors but also presents unique security challenges due to its interconnected and resource‐constrained devices. This study introduces the Bidirectional Gaussian Hummingbird Optimized End‐to‐End Blockchain (BGHO‐E2EB) model, designed detect classify cyberattacks within IoT environments. Unlike preventive approaches, developed model focuses on real‐time detection categorization attacks, enabling timely responses emerging threats. proposed integrates blockchain technology through Ethereum‐based smart contracts enhance integrity data exchanges networks. Additionally, a Artificial Algorithm employed for optimal feature selection, minimizing dimensionality computational load. A Long Short‐Term Memory (Bi‐LSTM) network further improves model's capability by accurately detecting categorizing cyber threats based selected features. Adam optimizer used efficient parameter tuning Bi‐LSTM network, ensuring high‐performance cyberattack detection. was evaluated using established benchmarks, including UNSW‐NB15, BOT‐IoT, NSL‐KDD datasets, accomplishing an accuracy 98.7%, precision 96.3%, level 99.5%, significantly outperforming traditional methods. These results demonstrate effectiveness BGHO‐E2EB as robust tool classifying in networks, making it suitable real‐world deployment dynamic environments where paramount.

Язык: Английский

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Optimal Design of Water Distribution Network Using Parallel Slime Mould Algorithm for Cost Minimization

European Journal of Theoretical and Applied Sciences, Год журнала: 2025, Номер 3(2), С. 334 - 347

Опубликована: Март 27, 2025

Water distribution networks (WDNs) are vital infrastructures designed to ensure a minimum acceptable supply level consumers under different operating conditions throughout the design period. Due their complexity and substantial investment required for construction maintenance, economic aspects have become primary focus researchers engineers. Various evolutionary algorithms (EAs), such as genetic algorithm (GA), been utilized achieve cost minimization while fulfilling hydraulic requirements. This study uses Parallel Slime Mould Algorithm (PSMA), variant of slime mould (SMA) developed by Wang et al., implemented solve mathematical optimization WDNs. The PSMA incorporates Hazen-Williams equation calculating head loss pressure constraints feasibility solution. proposed method is applied benchmark network compared with results from GA used Savic. proved effective in optimizing WDN, achieving reduction approximately 6.08% maintaining feasibility. However, pipe sizes showed notable differences, favoring larger diameters most pipes except 2. These highlight potential powerful tool WDN optimization, particularly when priority.

Язык: Английский

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