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

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4113 - 4159

Published: May 27, 2023

Language: Английский

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Boosted sooty tern optimization algorithm for global optimization and feature selection

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 213, P. 119015 - 119015

Published: Oct. 17, 2022

Language: 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, Journal Year: 2024, Volume and Issue: 131, P. 107881 - 107881

Published: Jan. 19, 2024

Language: 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, Journal Year: 2022, Volume and Issue: 254, P. 124363 - 124363

Published: May 27, 2022

Language: 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, Journal Year: 2022, Volume and Issue: 152, P. 106404 - 106404

Published: Dec. 6, 2022

Language: Английский

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

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(1), P. e0280006 - e0280006

Published: Jan. 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

Language: Английский

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

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

Published: March 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.

Language: 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, Journal Year: 2025, Volume and Issue: 93, P. 101844 - 101844

Published: Jan. 9, 2025

Language: Английский

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

Applied Intelligence, Journal Year: 2022, Volume and Issue: 53(6), P. 7232 - 7253

Published: July 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

Language: Английский

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

Mathematics, Journal Year: 2022, Volume and Issue: 10(11), P. 1929 - 1929

Published: June 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.

Language: Английский

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