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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A comprehensive survey of feature selection techniques based on whale optimization algorithm

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(16), P. 47775 - 47846

Published: Oct. 28, 2023

Language: Английский

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Boosting manta rays foraging optimizer by trigonometry operators: a case study on medical dataset

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(16), P. 9405 - 9436

Published: March 4, 2024

Language: Английский

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Golden Jackal and Enhanced Remora Optimization‐Based Sink Mobility for Energy Hole Mitigation in Underwater Wireless Sensor Network

International Journal of Communication Systems, Journal Year: 2025, Volume and Issue: 38(10)

Published: May 12, 2025

ABSTRACT Underwater wireless sensor networks (UWSNs) face challenges like limited bandwidth, high‐energy consumption, and the energy hole problem, where nodes near sinks deplete faster, reducing network performance lifespan. To address these issues, we propose hybrid optimization‐based sink mobility for mitigation in UWSNs (HOSEMU), utilizing a golden jackal optimization (GJO) enhanced remora (ERO) approach, termed GJERO. GJERO combines GJO's global search ERO's local to enhance cluster head (CH) selection, balancing consumption improving convergence. Sink toward energy‐depleted CHs further ensures efficient data collection extended Simulation results MATLAB show that HOSEMU outperforms existing methods, stability by 19.4% over energy‐efficient routing stable reporting algorithm (EERSDRA) 28.7% energy‐conserving (ECERO). These demonstrate HOSEMU's effectiveness addressing UWSN‐specific enhancing performance.

Language: Английский

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Mantis Search Algorithm Integrated with Opposition-Based Learning and Simulated Annealing for Feature Selection

Sustainable Machine Intelligence Journal, Journal Year: 2024, Volume and Issue: 8

Published: June 18, 2024

Feature selection (FS) plays a vital role in minimizing the high-dimensional data as much possible to aid enhancing classification accuracy and reducing computational costs. The purpose of FS techniques is extract most effective subset features, which might enable machine learning (ML) algorithms better grasp input data’s patterns improve their performance. Although several metaheuristic have been recently presented solve this problem, they still suffer from disadvantages, such getting stuck local optima, slow convergence speed, lack population diversity, prevent them achieving desired solutions an acceptable time. Therefore, study propose new feature approach, namely OBMSASA, based on integrating published mantis search algorithm with opposition-based (OBL) method simulated annealing (SA) strengthen its exploration exploitation operators. OBL aims operator, making able avoid stagnation into minima; meanwhile, SA used further thereby improving speed. K-nearest neighbor compute selected feature. proposed assessed using 21 common datasets compared rival optimizers terms performance metrics, including curve, average fitness, cost, length standard deviation, observe effectiveness efficiency. source code publicly accessible at https://drive.mathworks.com/OBMSASA.

Language: Английский

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Dynamic multi-swarm whale optimization algorithm based on elite tuning for high-dimensional feature selection classification problems

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112634 - 112634

Published: Dec. 1, 2024

Language: Английский

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MSBWO: A Multi-Strategies Improved Beluga Whale Optimization Algorithm for Feature Selection

Biomimetics, Journal Year: 2024, Volume and Issue: 9(9), P. 572 - 572

Published: Sept. 22, 2024

Feature selection (FS) is a classic and challenging optimization task in most machine learning data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic FS. To increase population diversity further improve the effectiveness of beluga whale (BWO) algorithm, this paper, we propose multi-strategies improved BWO (MSBWO), which incorporates circle mapping dynamic opposition-based (ICMDOBL) initialization as well elite pool (EP), step-adaptive Lévy flight spiral updating position (SLFSUP), golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes increasing during search process reducing risk falling into local optima. The EP technique also enhances algorithm's ability escape from SLFSUP, distinguished original BWO, aims rigor accuracy development spaces. Gold-SA introduced quality solutions. hybrid performance MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including qualitative analysis comparisons with other conventional state-of-the-art (SOTA) approaches that were 2024. results demonstrate superior algorithms terms maintains better balance between exploration exploitation. Moreover, according proposed continuous MSBWO, binary variant (BMSBWO) optimizers obtained function ten UCI datasets random forest (RF) classifier. Consequently, BMSBWO has proven very competitive classification precision feature reduction.

Language: Английский

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SCChOA: Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2023, Volume and Issue: 77(3), P. 3057 - 3075

Published: Jan. 1, 2023

Feature Selection (FS) is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However, due high dimensionality and complexity dataset, optimization algorithms for feature selection suffer balance issue during search process.Therefore, present paper proposes hybrid Sine-Cosine Chimp Optimization Algorithm (SCChOA) address problem.In this approach, firstly, multi-cycle iterative strategy designed better combine (SCA) (ChOA), enabling more effective in objective space.Secondly, S-shaped transfer function introduced perform binary transformation on SCChOA.Finally, SCChOA combined with K-Nearest Neighbor (KNN) classifier form novel wrapper method.To evaluate performance proposed method, 16 datasets different dimensions UCI repository along four evaluation metrics average fitness value, accuracy, number, running time are considered.Meanwhile, seven state-of-the-art metaheuristic solving chosen comparison.Experimental results demonstrate method outperforms other compared problem.It capable maximizing reduction number selected while maintaining accuracy.Furthermore, statistical tests also confirm significant effectiveness method.

Language: Английский

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A Method Based on Plants Light Absorption Spectrum and Its Use for Data Clustering

Journal of Bionic Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 4, 2024

Language: Английский

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