An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Artificial Intelligence Review, Год журнала: 2023, Номер 56(11), С. 13187 - 13257

Опубликована: Апрель 9, 2023

Язык: Английский

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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

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Utilizing convolutional neural networks to classify monkeypox skin lesions

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Сен. 3, 2023

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the be challenging time-consuming, especially resource-limited settings where laboratory tests may not available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential image recognition classification tasks. To this end, study proposes an approach using CNNs to classify lesions. Additionally, optimized CNN model Grey Wolf Optimizer (GWO) algorithm, resulting significant improvement accuracy, precision, recall, F1-score, AUC compared non-optimized model. The GWO optimization strategy enhance performance models similar achieved impressive accuracy 95.3%, indicating optimizer has improved model's ability discriminate between positive negative classes. proposed several benefits for improving efficiency diagnosis surveillance. It could enable faster more accurate lesions, leading earlier detection better patient outcomes. Furthermore, crucial public health implications controlling preventing outbreaks. Overall, offers novel highly effective monkeypox, which real-world applications.

Язык: Английский

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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

Journal of Bionic Engineering, Год журнала: 2023, Номер 20(4), С. 1766 - 1790

Опубликована: Фев. 7, 2023

Язык: Английский

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Optimal truss design with MOHO: A multi-objective optimization perspective

PLoS ONE, Год журнала: 2024, Номер 19(8), С. e0308474 - e0308474

Опубликована: Авг. 19, 2024

This research article presents the Multi-Objective Hippopotamus Optimizer (MOHO), a unique approach that excels in tackling complex structural optimization problems. The (HO) is novel meta-heuristic methodology draws inspiration from natural behaviour of hippos. HO built upon trinary-phase model incorporates mathematical representations crucial aspects Hippo's behaviour, including their movements aquatic environments, defense mechanisms against predators, and avoidance strategies. conceptual framework forms basis for developing multi-objective (MO) variant MOHO, which was applied to optimize five well-known truss structures. Balancing safety precautions size constraints concerning stresses on individual sections constituent parts, these problems also involved competing objectives, such as reducing weight structure maximum nodal displacement. findings six popular methods were used compare results. Four industry-standard performance measures this comparison qualitative examination finest Pareto-front plots generated by each algorithm. average values obtained Friedman rank test analysis unequivocally showed MOHO outperformed other resolving significant quickly. In addition finding preserving more Pareto-optimal sets, recommended algorithm produced excellent convergence variance objective decision fields. demonstrated its potential navigating objectives through diversity analysis. Additionally, swarm effectively visualize MOHO's solution distribution across iterations, highlighting superior behaviour. Consequently, exhibits promise valuable method issues.

Язык: Английский

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Particle guided metaheuristic algorithm for global optimization and feature selection problems

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123362 - 123362

Опубликована: Фев. 1, 2024

Язык: Английский

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Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems

Heliyon, Год журнала: 2024, Номер 10(11), С. e31629 - e31629

Опубликована: Май 24, 2024

This paper introduces a new metaheuristic technique known as the Greater Cane Rat Algorithm (GCRA) for addressing optimization problems. The process of GCRA is inspired by intelligent foraging behaviors greater cane rats during and off mating season. Being highly nocturnal, they are intelligible enough to leave trails forage through reeds grass. Such would subsequently lead food water sources shelter. exploration phase achieved when different shelters scattered around their territory trails. It presumed that alpha male maintains knowledge about these routes, result, other modify location according this information. Also, males aware breeding season separate themselves from group. assumption once group separated season, activities concentrated within areas abundant sources, which aids exploitation. Hence, smart paths mathematically represented realize design GCR algorithm carry out tasks. performance tested using twenty-two classical benchmark functions, ten CEC 2020 complex 2011 real-world continuous To further test proposed algorithm, six classic problems in engineering domain were used. Furthermore, thorough analysis computational convergence results presented shed light on efficacy stability levels GCRA. statistical significance compared with state-of-the-art algorithms Friedman's Wilcoxon's signed rank tests. These findings show produced optimal or nearly solutions evaded trap local minima, distinguishing it rival employed tackle similar optimizer source code publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/165241-greater-cane-rat-algorithm-gcra

Язык: Английский

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Efficient parameter extraction of photovoltaic models with a novel enhanced prairie dog optimization algorithm

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

Опубликована: Апрель 4, 2024

Abstract The growing demand for solar energy conversion underscores the need precise parameter extraction methods in photovoltaic (PV) plants. This study focuses on enhancing accuracy PV system extraction, essential optimizing models under diverse environmental conditions. Utilizing primary (single diode, double and three diode) module models, research emphasizes importance of accurate identification. In response to limitations existing metaheuristic algorithms, introduces enhanced prairie dog optimizer (En-PDO). novel algorithm integrates strengths (PDO) with random learning logarithmic spiral search mechanisms. Evaluation against PDO, a comprehensive comparison eighteen recent spanning optimization techniques, highlight En-PDO’s exceptional performance across different cell CEC2020 functions. Application En-PDO single using experimental datasets (R.T.C. France silicon Photowatt-PWP201 cells) test functions, demonstrates its consistent superiority. achieves competitive or superior root mean square error values, showcasing efficacy accurately modeling behavior cells performing optimally These findings position as robust reliable approach estimation emphasizing potential advancements compared algorithms.

Язык: Английский

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Review of the metaheuristic algorithms in applications: Visual analysis based on bibliometrics (1994–2023)

Expert Systems with Applications, Год журнала: 2024, Номер 255, С. 124857 - 124857

Опубликована: Июль 23, 2024

Язык: Английский

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Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023)

Energy Strategy Reviews, Год журнала: 2024, Номер 53, С. 101409 - 101409

Опубликована: Май 1, 2024

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling forecasting tasks. While excels in capturing intricate patterns data, it may falter achieving optimality due to nonlinear nature energy data. Conversely, offer optimization capabilities but suffer from computational burdens, especially with high-dimensional This paper provides comprehensive review spanning 2018 2023, examining integration within frameworks applications. We analyze state-of-the-art techniques, innovations, recent advancements, identifying open research challenges. Additionally, we propose novel framework that seamlessly merges into paradigms, aiming enhance performance efficiency addressing problems. contributions include: 1. Overview advancements MHs, DL, integration. 2. Coverage trends 2023. 3. Introduction Alpha metric evaluation. 4. Innovative harmonizing MHs DL

Язык: Английский

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