Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

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

Опубликована: Фев. 29, 2024

Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. HO is conceived by drawing inspiration from inherent behaviors observed hippopotamuses, showcasing an innovative approach metaheuristic methodology. conceptually defined using trinary-phase model that incorporates their position updating rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained top rank 115 out 161 benchmark functions finding optimal value, encompassing unimodal high-dimensional multimodal functions, fixed-dimensional as well CEC 2019 test suite 2014 dimensions 10, 30, 50, 100 Zigzag Pattern suggests demonstrates noteworthy proficiency both exploitation exploration. Moreover, it effectively balances exploration exploitation, supporting search process. In light results addressing four distinct engineering design challenges, has achieved most efficient resolution while concurrently upholding adherence to designated constraints. performance evaluation algorithm encompasses various aspects, including comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, IWO recognized extensively researched metaheuristics, AOA recently developed algorithms, CMA-ES high-performance optimizers acknowledged for success IEEE competition. According statistical post hoc analysis, determined be significantly superior investigated algorithms. source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .

Язык: Английский

---

Adaptive Gbest-Guided Atom Search Optimization for Designing Stable Digital IIR Filters

Circuits Systems and Signal Processing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 25, 2025

Язык: Английский

---

Optimal parameters estimation of lithium-ion battery in smart grid applications based on gazelle optimization algorithm

Energy, Год журнала: 2023, Номер 285, С. 129509 - 129509

Опубликована: Окт. 30, 2023

Язык: Английский

---

Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems

Expert Systems with Applications, Год журнала: 2024, Номер 256, С. 124882 - 124882

Опубликована: Июль 29, 2024

Язык: Английский

---

Computer Aided Cervical Cancer Diagnosis Using Gazelle Optimization Algorithm With Deep Learning Model

IEEE Access, Год журнала: 2024, Номер 12, С. 13046 - 13054

Опубликована: Янв. 1, 2024

Cervical cancer (CC), the most common among women, is commonly diagnosed through Pap smears, a crucial screening process that includes collecting cervical cells for examination. Artificial intelligence (AI)-powered computer-aided diagnoses (CAD) system becomes promising tool improving CC diagnosis. Deep learning (DL), branch of AI, holds particular potential in CAD systems early detection and accurate DL algorithm trained to identify abnormalities patterns smear images, such as dysplasia, cellular changes, other markers CC. So, this study presents Computer Aided Cancer Diagnosis utilizing Gazelle Optimizer Algorithm with Learning (CACCD-GOADL) model on images. The foremost objective CACCD-GOADL approach examine image To accomplish this, methodology uses an improved MobileNetv3 extracting complex In addition, technique designs new GOA hyperparameter tuning system. For classification identification cancer, stacked extreme machine (SELM) methodology. simulation validation verified benchmark dataset Herlev. Experimental results highlighted reaches superior outcomes over methods.

Язык: Английский

---

Efficient Speed Control for DC Motors Using Novel Gazelle Simplex Optimizer

IEEE Access, Год журнала: 2023, Номер 11, С. 105830 - 105842

Опубликована: Янв. 1, 2023

This paper addresses the design of an optimally executed proportional-integral-derivative (PID) controller, tailored for speed regulation a direct current (DC) motor. To achieve this objective, we present novel hybrid algorithm, combining gazelle optimization algorithm (GOA) with effective simplex search method known as Nelder-Mead (NM) technique. The fusion these algorithms yields innovative hybridized version, striking balance between exploration and exploitation. proposed approach, named optimizer (GSO), showcases great promise when applied to task controlling DC motor using PID controller. identify optimal values gains, harness power objective function well, which guides GSO in determining most favorable controller settings. Rigorous comparative simulations are then undertaken, where pit against several other algorithms, namely reptile prairie dog weighted mean vectors optimization, original GOA algorithm. These allow us assess system's behavior through various lenses, such statistical tests, time frequency domain responses, robustness analysis, changes function. evaluations from comprehensive tests demonstrate superiority GSO-based controlled system. exhibits better performance than alternative providing solid evidence its effectiveness. Furthermore, approach outperforms reported tuning methods, affirming prowess achieving superior motors.

Язык: Английский

---

Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 434, С. 117588 - 117588

Опубликована: Ноя. 29, 2024

Язык: Английский

---

Deep dyna reinforcement learning based energy management system for solar operated hybrid electric vehicle using load scheduling technique

Journal of Energy Storage, Год журнала: 2024, Номер 102, С. 114106 - 114106

Опубликована: Окт. 18, 2024

Язык: Английский

---

An improved artificial rabbits optimization for accurate and efficient infinite impulse response system identification

Decision Analytics Journal, Год журнала: 2023, Номер 9, С. 100355 - 100355

Опубликована: Ноя. 3, 2023

Identifying models with Infinite Impulse Response (IIR) is crucial in signal processing and system identification. This paper addresses the challenges of IIR model identification by proposing an improved version Artificial Rabbits Optimization (ARO) algorithm called ARO (IARO). The IARO integrates adaptive local search mechanism experience-based perturbed learning strategy as two key enhancements to improve effectiveness ARO. These additions aim address loss accuracy during iterations algorithm's ability exploit promising areas. Four benchmark examples different plants are considered, performance proposed compared existing competitive methods. results consistently demonstrate that outperforms convergence for across all orders systems. Visual analysis, curves, coefficient comparison, statistical metrics comparison validate superiority algorithm. Additionally, Wilcoxon signed-rank test provide further evidence supporting superior IARO. comprehensive analysis showcases efficacy accurately identifying work represents a significant advancement identification, offering methodology accurate efficient modeling.

Язык: Английский

---

Reduced order infinite impulse response system identification using manta ray foraging optimization

Alexandria Engineering Journal, Год журнала: 2024, Номер 87, С. 448 - 477

Опубликована: Янв. 1, 2024

This article presents a useful application of the Manta Ray Foraging Optimization (MRFO) algorithm for solving adaptive infinite impulse response (IIR) system identification problem. The effectiveness proposed technique is validated on four benchmark IIR models reduced order identification. stability estimated assured by incorporating pole-finding and initialization routine in search procedure MRFO this algorithmic modification contributes to when seeking stable filter solutions. absence such scheme, which primarily case with majority recently published literature, may lead generation an unstable unknown real-world instances (particularly estimation increases). Experiments conducted study highlight that helps achieve even though large bounds design variables are considered. convergence rate, robustness, computational speed all considered problems investigated. influence control parameters performances evaluated gain insight into interaction between three foraging strategies algorithm. Extensive statistical performance analyses employing various non-parametric hypothesis tests concerning consistency comparison MRFO-based approach six other metaheuristic procedures investigate efficiency. results mean square error metric also improved solution quality compared techniques literature.

Язык: Английский

---