A Novel Meta-Heuristic Algorithm Based on Birch Succession in the Optimization of an Electric Drive with a Flexible Shaft DOI Creative Commons
Mateusz Malarczyk, Seiichiro Katsura, Marcin Kamiński

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(16), P. 4104 - 4104

Published: Aug. 18, 2024

The paper presents the application of a new bio-inspired metaheuristic optimization algorithm. popularity and usability different swarm-based algorithms are undeniable. majority known mimic hunting behavior animals. However, current approach does not satisfy full bio-diversity inspiration among organisms. Thus, Birch-inspired Optimization Algorithm (BiOA) is proposed as powerful efficient tool based on pioneering one most common tree species. Birch trees for their superiority over other species in overgrowing spreading across unrestricted terrains. two-step algorithm reproduces both seed transport plant development. A detailed description mathematical model given. discussion examination influence parameters efficiency also provided detail. In order to demonstrate effectiveness algorithm, its selecting control structure drive system with an elastic connection shown. PI controller two additional feedbacks torque speed difference between motor working machine was selected. rated variable considered. theoretical considerations simulation study were verified laboratory stand.

Language: Английский

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions DOI
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

Citations

61
Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022) DOI
Ali Mohammadi, Farid Sheikholeslam

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 126, P. 106959 - 106959

Published: Nov. 1, 2023

Language: Английский

Citations

26
Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines DOI Creative Commons
Elvis Han Cui, Zizhao Zhang,

C. Chen

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 24, 2024

Abstract Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and increasingly used across disciplines to tackle various types challenging optimization problems. This paper demonstrates the usefulness such for solving a variety problems in statistics using nature-inspired algorithm called competitive swarm optimizer with mutated agents (CSO-MA). was proposed by one authors its superior performance relative many competitors had been demonstrated earlier work again this paper. The main goal is show typical algorithmi, like CSO-MA, efficient tackling different statistics. Our applications new include finding maximum likelihood estimates parameters single cell generalized trend model study pseudotime bioinformatics, estimating commonly Rasch education research, M-estimates Cox regression Markov renewal model, performing matrix completion tasks impute missing data two compartment selecting variables optimally an ecology problem China. To further demonstrate flexibility metaheuristics, we also find optimal design car refueling experiment auto industry logistic multiple interacting factors. In addition, that metaheuristics can sometimes outperform

Language: Английский

Citations

13
Model Parameters Estimation for the Biosciences Using Particle Swarm Optimization DOI
Jun-Hyung Park, Sisi Shao, Weng Kee Wong

et al.

Statistics in Biosciences, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

1
Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding DOI Open Access
Rebika Rai, Arunita Das, Swarnajit Ray

et al.

Archives of Computational Methods in Engineering, Journal Year: 2022, Volume and Issue: 29(7), P. 5313 - 5352

Published: June 7, 2022

Language: Английский

Citations

36
Genetic algorithm based probabilistic model for agile project success in global software development DOI
Mohammad Shameem, Mohammad Nadeem, Abu Taha Zamani

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 135, P. 109998 - 109998

Published: Jan. 14, 2023

Language: Английский

Citations

19
Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation DOI Creative Commons
Naoko Koide–Majima, Shinji Nishimoto, Kei Majima

et al.

Neural Networks, Journal Year: 2023, Volume and Issue: 170, P. 349 - 363

Published: Nov. 9, 2023

Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful imagery, and visualizable been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing for arbitrary natural stands significant milestone. In this study, we achieved enhancing previous method. Specifically, demonstrated that visual image reconstruction method proposed in seminal study Shen et al. (2019) heavily relied on low-level information decoded could not efficiently utilize semantic would recruited during imagery. To address limitation, extended Bayesian estimation framework introduced assistance into it. Our successfully both seen (i.e., those eye) imagined Quantitative evaluation showed our identify highly accurately compared chance accuracy (seen: 90.7%, imagery: 75.6%, accuracy: 50.0%). contrast, only 64.3%, 50.4%). These results suggest provide unique tool directly investigating subjective contents illusions, hallucinations, dreams.

Language: Английский

Citations

14
XECryptoGA: a metaheuristic algorithm-based block cipher to enhance the security goals DOI
Md Saquib Jawed, Mohammad Sajid

Evolving Systems, Journal Year: 2022, Volume and Issue: 14(5), P. 749 - 770

Published: Sept. 21, 2022

Language: Английский

Citations

19
Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems DOI Creative Commons
Ishaani Priyadarshini

Biomimetics, Journal Year: 2024, Volume and Issue: 9(3), P. 130 - 130

Published: Feb. 21, 2024

In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired algorithms represent highly valuable pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), novel algorithm inspired by natural branching patterns. DGO offers solution for intricate problems demonstrates its efficiency in exploring diverse spaces. The has been extensively tested with suite of machine learning algorithms, deep metaheuristic the results, both before after optimization, unequivocally support proposed algorithm’s feasibility, effectiveness, generalizability. Through empirical validation using established datasets like diabetes breast cancer, consistently enhances model performance across various domains. Beyond working experimental analysis, DGO’s wide-ranging applications learning, logistics, engineering solving real-world have highlighted. study also considers implications implementing multiple scenarios. As remains crucial research industry, emerges as promising avenue innovation problem solving.

Language: Английский

Citations

4
Integrating metaheuristics and artificial intelligence for healthcare: basics, challenging and future directions DOI Creative Commons
Essam H. Houssein, Eman Saber, Abdelmgeid A. Ali

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 12, 2024

Abstract Accurate and rapid disease detection is necessary to manage health problems early. Rapid increases in data amount dimensionality caused challenges many disciplines, with the primary issues being high computing costs, memory low accuracy performance. These will arise since Machine Learning (ML) classifiers are mostly used these fields. However, noisy irrelevant features have an impact on ML accuracy. Therefore, choose best subset of decrease data, Metaheuristics (MHs) optimization algorithms applied Feature Selection (FS) using various modalities medical imaging or datasets different dimensions. The review starts by giving a general overview approaches AI algorithms, followed MH for healthcare applications, analysis MHs boosted wide range research databases as source access numerous field publications. final section this discusses facing application development.

Language: Английский

Citations

4