A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems

Özbay, Feyza Altunbey

doi:10.1016/j.jestch.2023.101408

Cited by 17 publications

(3 citation statements)

References 83 publications

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“…Özbay 4 Modified seahorse optimization algorithm Engineering design problems 55 Electromagnetism optimization Multi-level thresholding…”

Section: Reference No Optimization Algorithm Used Applicationmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

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This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ($$Q^{AB/F}$$ Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ($$N^{AB/F}$$ N A B / F ). The average $$Q^{AB/F} = 0.765508$$ Q A B / F = 0.765508 , $$SCD = 1.63185$$ S C D = 1.63185 , $$SSIM = 0.726317$$ S S I M = 0.726317 , and $$N^{AB/F} = 0.006617$$ N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

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“…Özbay 4 Modified seahorse optimization algorithm Engineering design problems 55 Electromagnetism optimization Multi-level thresholding…”

Section: Reference No Optimization Algorithm Used Applicationmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

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“…The Enhanced Sea Horse Optimization (ESHO) technique was used to improve the accuracy of the QRNN approach. Sea Horse Optimization (SHO) is motivated by the breeding, natural displacement, and predation strategies of Sea Horses (SHs) [25]. This technique has three major elements: mobility predation, and breeding.…”

Section: E Hyperparameter Tuning Using the Esho Algorithmmentioning

confidence: 99%

Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

Anandhi,

Sathiamoorthy

2023

Eng. Technol. Appl. Sci. Res.

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The detection of diseases in rice plants is an essential step in ensuring healthy crop growth and maximizing yields. A real-time and accurate plant disease detection technique can assist in the development of mitigation strategies to ensure food security on a large scale and economical rice crop protection. An accurate classification of rice plant diseases using DL and computer vision could create a foundation to achieve a site-specific application of agrochemicals. Image investigation tools are efficient for the early diagnosis of plant diseases and the continuous monitoring of plant health status. This article presents an Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion for Rice Plant Disease Detection and Classification (ESHODL-MFRPDC) technique. The proposed technique employed a DL-based fusion process with a hyperparameter tuning strategy to achieve an improved rice plant disease detection performance. The ESHODL-MFRPDC approach used Bilateral Filtering (BF)-based noise removal and contrast enhancement as a preprocessing step. Furthermore, Mayfly Optimization (MFO) with a Multi-Level Thresholding (MLT) based segmentation process was used to recognize the diseased portions in the leaf image. A fusion of three DL models was used for feature extraction, namely Residual Network (ResNet50), Xception, and NASNet. The Quasi-Recurrent Neural Network (QRNN) was used for the recognition of rice plant diseases, and its hyperparameters were set using the ESHO method. The performance of the ESHODL-MFRPDC method was validated using the rice leaf disease dataset from the UCI database. An extensive comparison study demonstrated the promising performance of the proposed method over others.

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“…With the ongoing evolution of optimization technology, significant advancements have been achieved in addressing complex, real−world engineering conundrums [25]. Furthermore, a plethora of novel optimization methodologies have been effectively integrated into a myriad of engineering design processes [26]. Among these, the design of pressure vessels is recognized as a quintessential problem [27], with the primary objective of minimizing overall design costs.…”

Section: Introductionmentioning

confidence: 99%

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

Li,

Sun

2023

Applied Sciences

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The Gray Wolf Optimizer (GWO) is an established algorithm for addressing complex optimization tasks. Despite its effectiveness, enhancing its precision and circumventing premature convergence is crucial to extending its scope of application. In this context, our study presents the Cauchy Gray Wolf Optimizer (CGWO), a modified version of GWO that leverages Cauchy distributions for key algorithmic improvements. The innovation of CGWO lies in several areas: First, it adopts a Cauchy distribution-based strategy for initializing the population, thereby broadening the global search potential. Second, the algorithm integrates a dynamic inertia weight mechanism, modulated non-linearly in accordance with the Cauchy distribution, to ensure a balanced trade-off between exploration and exploitation throughout the search process. Third, it introduces a Cauchy mutation concept, using inertia weight as a probability determinant, to preserve diversity and bolster the capability for escaping local optima during later search phases. Furthermore, a greedy strategy is employed to incrementally enhance solution accuracy. The performance of CGWO was rigorously evaluated using 23 benchmark functions, demonstrating significant improvements in convergence rate, solution precision, and robustness when contrasted with conventional algorithms. The deployment of CGWO in solving the engineering challenge of pressure vessel design illustrated its superiority over traditional methods, highlighting its potential for widespread adoption in practical engineering contexts.

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A modified seahorse optimization algorithm based on chaotic maps for solving global optimization and engineering problems

Cited by 17 publications

References 83 publications

A hybrid swarm intelligence algorithm for region-based image fusion

A hybrid swarm intelligence algorithm for region-based image fusion

Enhanced Sea Horse Optimization with Deep Learning-based Multimodal Fusion Technique for Rice Plant Disease Segmentation and Classification

Pressure Vessel Design Problem Using Improved Gray Wolf Optimizer Based on Cauchy Distribution

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