A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(7), С. 4113 - 4159

Опубликована: Май 27, 2023

Язык: Английский

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Boosted sooty tern optimization algorithm for global optimization and feature selection

Expert Systems with Applications, Год журнала: 2022, Номер 213, С. 119015 - 119015

Опубликована: Окт. 17, 2022

Язык: Английский

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A comprehensive review of synthetic data generation in smart farming by using variational autoencoder and generative adversarial network

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107881 - 107881

Опубликована: Янв. 19, 2024

Язык: Английский

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Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization

Energy, Год журнала: 2022, Номер 254, С. 124363 - 124363

Опубликована: Май 27, 2022

Язык: Английский

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A modified reptile search algorithm for global optimization and image segmentation: Case study brain MRI images

Computers in Biology and Medicine, Год журнала: 2022, Номер 152, С. 106404 - 106404

Опубликована: Дек. 6, 2022

Язык: Английский

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MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

PLoS ONE, Год журнала: 2023, Номер 18(1), С. e0280006 - e0280006

Опубликована: Янв. 3, 2023

Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single strategy and control parameter affect convergence balance between exploration exploitation. Since strategies have considerable impact on performance of algorithms, collaborating multiple can significantly enhance abilities algorithms. This our motivation to propose multi-trial vector-based monkey named MMKE. It introduces novel best-history trial vector producer (BTVP) random (RTVP) that effectively collaborate with canonical MKE (MKE-TVP) using approach tackle various real-world optimization problems diverse challenges. expected proposed MMKE improve global search capability, strike exploitation, prevent original from converging prematurely during process. The was assessed CEC 2018 test functions, results were compared eight metaheuristic As result experiments, it demonstrated capable producing competitive superior terms accuracy rate comparison comparative Additionally, Friedman used examine gained experimental statistically, proving Furthermore, four engineering design optimal power flow (OPF) problem for IEEE 30-bus system are optimized demonstrate MMKE's real applicability. showed handle difficulties associated able solve multi-objective OPF better solutions than

Язык: Английский

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Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Март 5, 2024

Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve optimization capabilities of conventional optimizer in order address problem data clustering. The process that groups similar items within dataset into non-overlapping groups. Grey hunting behaviour served as model for however, it frequently lacks exploration and exploitation are essential efficient work mainly focuses on enhancing using weight factor concepts increase variety avoid premature convergence. Using partitional clustering-inspired fitness function, was extensively evaluated ten numerical functions multiple real-world datasets with varying levels complexity dimensionality. methodology is based incorporating concept purpose refining initial solutions adding diversity during phase. results show performs much better than standard discovering optimal clustering solutions, indicating higher capacity effective solution space. found able produce high-quality cluster centres fewer iterations, demonstrating its efficacy efficiency various datasets. Finally, demonstrates robustness dependability resolving issues, which represents significant advancement over techniques. In addition addressing shortcomings algorithm, incorporation innovative establishes further metaheuristic algorithms. performance around 34% original both test problems problems.

Язык: Английский

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A reinforcement learning-based ranking teaching-learning-based optimization algorithm for parameters estimation of photovoltaic models

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101844 - 101844

Опубликована: Янв. 9, 2025

Язык: Английский

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Development and application of equilibrium optimizer for optimal power flow calculation of power system

Applied Intelligence, Год журнала: 2022, Номер 53(6), С. 7232 - 7253

Опубликована: Июль 18, 2022

This paper proposes an enhanced version of Equilibrium Optimizer (EO) called (EEO) for solving global optimization and the optimal power flow (OPF) problems. The proposed EEO algorithm includes a new performance reinforcement strategy with Lévy Flight mechanism. addresses shortcomings original aims to provide better solutions (than those provided by EO) problems, especially OPF efficiency was confirmed comparing its results on ten functions CEC'20 test suite, other algorithms, including high-performance i.e., CMA-ES, IMODE, AGSK LSHADE_cnEpSin. Moreover, statistical significance these validated Wilcoxon's rank-sum test. After that, applied solve problem. is formulated as nonlinear problem conflicting objectives subjected both equality inequality constraints. this technique deliberated evaluated standard IEEE 30-bus system different objectives. obtained compared EO using techniques mentioned in literature. These Simulation revealed that provides optimized than 20 published methods well algorithm. superiority demonstrated through six cases, involved minimization objectives: fuel cost, cost valve-point loading effect, emission, total active losses, voltage deviation, instability. Also, comparison indicate can robust, high-quality feasible

Язык: Английский

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Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

Mathematics, Год журнала: 2022, Номер 10(11), С. 1929 - 1929

Опубликована: Июнь 4, 2022

Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence redundant and irrelevant features in negatively influences algorithms leads decreases performance algorithms. Using effective data mining analyzing tasks such as classification can increase accuracy results relevant decisions made by decision-makers using them. This become more acute when dealing challenging, large-scale problems medical applications. Nature-inspired metaheuristics show superior finding optimal feature subsets literature. As a seminal attempt, wrapper selection approach is presented on basis newly proposed Aquila optimizer (AO) this work. In regard, uses AO search algorithm order discover most subset. S-shaped binary (SBAO) V-shaped (VBAO) are two suggested for datasets. Binary position vectors generated utilizing S- transfer functions while space stays continuous. compared six recent optimization seven benchmark comparison comparative algorithms, gained demonstrate that both BAO variants improve these also tested real-dataset COVID-19. findings testified SBAO outperforms regarding least number selected highest accuracy.

Язык: Английский

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