Snake Optimizer: A novel meta-heuristic optimization algorithm

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 242, P. 108320 - 108320

Published: Feb. 8, 2022

Language: Английский

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Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization

Knowledge-Based Systems, Journal Year: 2022, Volume and Issue: 260, P. 110146 - 110146

Published: Nov. 29, 2022

Language: Английский

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An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 149, P. 106075 - 106075

Published: Sept. 6, 2022

Language: Английский

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Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection

Neurocomputing, Journal Year: 2022, Volume and Issue: 503, P. 325 - 362

Published: June 28, 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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Improved bald eagle search algorithm for global optimization and feature selection

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 68, P. 141 - 180

Published: Jan. 18, 2023

The use of metaheuristics is one the most encouraging methodologies for taking care real-life problems. Bald eagle search (BES) algorithm latest swarm-intelligence metaheuristic inspired by intelligent hunting behavior bald eagles. In recent research works, BES has performed reasonably well over a wide range application areas such as chemical engineering, environmental science, physics and astronomy, structural modeling, global optimization, engineering design, energy efficiency, etc. However, it still lacks adequate searching efficiency tendency to stuck in local optima which affects final outcome. This paper introduces modified (mBES) that removes shortcomings original incorporating three improvements; Opposition-based learning (OBL), Chaotic Local Search (CLS), Transition & Pharsor operators. OBL embedded different phases standard viz. initial population, selecting, space, swooping update positions individual solutions strengthen exploration, CLS used enhance position best agent will lead enhancing all individuals, operators help provide sufficient exploration–exploitation trade-off. mBES initially evaluated with 29 CEC2017 10 CEC2020 optimization benchmark functions. addition, practicality tested real-world feature selection problem five design Results are compared against number classical algorithms using statistical metrics, convergence analysis, box plots, Wilcoxon rank sum test. case composite test functions F21-F30, wins 70% cases, whereas rest functions, generates good results 65% cases. proposed produces performance 55% 45% generated competitive results. On other hand, problems, among algorithms. problem, also showed competitiveness observations problems show superiority robustness baseline metaheuristics. It can be safely concluded improvements suggested proved effective making enough solve variety

Language: Английский

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Hierarchical Harris hawks optimizer for feature selection

Journal of Advanced Research, Journal Year: 2023, Volume and Issue: 53, P. 261 - 278

Published: Jan. 20, 2023

Feature selection is a typical NP-hard problem. The main methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must swarm intelligence algorithm, performance in feature closely related to algorithm's quality. Therefore, it essential choose design suitable algorithm improve based on wrapper. Harris hawks optimization (HHO) superb approach that has just been introduced. It high convergence rate powerful global search capability but an unsatisfactory effect dimensional problems or complex problems. we introduced hierarchy HHO's ability deal with selection. To make obtain good accuracy fewer features run faster selection, improved HHO named EHHO. On 30 UCI datasets, (EHHO) can achieve very classification less running time features. We first conducted extensive experiments 23 classical benchmark functions compared EHHO many state-of-the-art metaheuristic algorithms. Then transform into binary (bEHHO) through conversion function verify extraction data sets. Experiments show better speed minimum than other peers. At same time, HHO, significantly weakness dealing functions. Moreover, datasets repository, bEHHO comparative Compared original bHHO, excellent also bHHO time.

Language: Английский

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An improved algorithm optimization algorithm based on RungeKutta and golden sine strategy

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 247, P. 123262 - 123262

Published: Jan. 25, 2024

Language: Английский

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WHHO: enhanced Harris hawks optimizer for feature selection in high-dimensional data

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Electronics, Journal Year: 2022, Volume and Issue: 11(12), P. 1919 - 1919

Published: June 20, 2022

The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based performs optimization procedure using novel way exploration and exploitation multiphases search. In this review research, we focused on applications developments well-established robust (HHO) as one most popular techniques 2020. Moreover, several experiments were carried out to prove powerfulness effectivness HHO compared with nine other state-of-art algorithms Congress Evolutionary Computation (CEC2005) CEC2017. literature paper includes deep insight about possible future directions ideas worth investigations regarding new variants its widespread applications.

Language: Английский

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