Hybrid beluga whale optimization algorithm with multi-strategy for functions and engineering optimization problems

Journal Of Big Data, Год журнала: 2024, Номер 11(1)

Опубликована: Янв. 2, 2024

Abstract Beluga Whale Optimization (BWO) is a new metaheuristic algorithm that simulates the social behaviors of beluga whales swimming, foraging, and whale falling. Compared with other optimization algorithms, BWO shows certain advantages in solving unimodal multimodal problems. However, convergence speed performance still have some deficiencies when complex multidimensional Therefore, this paper proposes hybrid method called HBWO combining Quasi-oppositional based learning (QOBL), adaptive spiral predation strategy, Nelder-Mead simplex search (NM). Firstly, initialization phase, QOBL strategy introduced. This reconstructs initial spatial position population by pairwise comparisons to obtain more prosperous higher quality population. Subsequently, an designed exploration exploitation phases. The first learns optimal individual positions dimensions through avoid loss local optimality. At same time, movement motivated cosine factor introduced maintain balance between exploitation. Finally, NM added. It corrects multiple scaling methods improve accurately efficiently. verified utilizing CEC2017 CEC2019 test functions. Meanwhile, superiority six engineering design examples. experimental results show has feasibility effectiveness practical problems than methods.

Язык: Английский

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Random Walk‐Based GOOSE Algorithm for Solving Engineering Structural Design Problems

Engineering Reports, Год журнала: 2025, Номер 7(5)

Опубликована: Апрель 30, 2025

ABSTRACT The proposed Random Walk‐based Improved GOOSE (IGOOSE) search algorithm is a novel population‐based meta‐heuristic inspired by the collective movement patterns of geese and stochastic nature random walks. This includes inherent balance between exploration exploitation integrating walk behavior with local strategies. In this paper, IGOOSE has been rigorously tested across 23 benchmark functions where 13 benchmarks are varying dimensions (10, 30, 50, 100 dimensions). These provide diverse range optimization landscapes, enabling comprehensive evaluation performance under different problem complexities. various parameters such as convergence speed, magnitude solution, robustness for dimensions. Further, applied to optimize eight distinct engineering problems, showcasing its versatility effectiveness in real‐world scenarios. results these evaluations highlight competitive tool, offering promising both standard complex structural problems. Its ability effectively, combined deal positions valuable tool.

Язык: Английский

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Fuzzy C-Means Algorithm Modification Based on Distance Measurement for River Water Quality

Kinetik Game Technology Information System Computer Network Computing Electronics and Control, Год журнала: 2024, Номер unknown

Опубликована: Июль 19, 2024

River water quality could be determined by understanding the capacity of pollutants in a body. Fuzzy C-Means (FCM) is one fuzzy clustering methods for determining river measuring parameters, that is, dissolved oxygen (DO) and total solids (TDS). The FCM algorithm an effective grouping data but often produces local inconsistent optimal solutions due to partition matrix's random initialisation process. Therefore, this study proposes modify precise matrix process using several distance concepts. purpose proposed modification get more consistent results minimise stop iterations. validation uses algorithm, three namely Partition Coefficient Index (PCI), Entropy (PEI) Silhouette Score (SS). experiments were conducted with replications various showed number iterations stopped has different values PCI, PEI, SS, objective functions each trial. On contrary, SS values, stops fewer modified initialising can used C-means algorithm.

Язык: Английский

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A Novel Comparative Analysis of Fuzzy C-Mean and Region Growing in Spine Image Segmentation

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 12, 2024

The authors have requested that this preprint be removed from Research Square.

Язык: Английский

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Novel Comparative Analysis of Fuzzy C-Mean and Region Growing in Spine Image Segmentation

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Апрель 22, 2024

Medical image segmentation plays a crucial role in various clinical applications, including disease diagnosis and treatment planning. In the context of spine imaging, accurate is essential for precise analysis intervention. This study presents comparative two prominent algorithms: fuzzy c-means (FCM) region growing, applied to segmentation. The dataset consists images obtained from medical imaging modalities, preprocessed enhance clarity remove noise. Both FCM region-growing algorithms are implemented with appropriate parameter settings evaluated using quantitative metrics such as Dice similarity coefficient, sensitivity, specificity. Additionally, qualitative assessments conducted through visual inspection segmented images. results reveal distinct performance characteristics each algorithm, highlighting their respective strengths weaknesses tasks. Through comprehensive discussion, this provides valuable insights into effectiveness algorithms, aiding clinicians researchers selecting suitable approaches applications.

Язык: Английский

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The Sobel Operator Combined with Double-Input U-Net Model for Lung Nodule Segmentation

Опубликована: Янв. 1, 2024

Язык: Английский

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