Applying a Hybrid Gray Wolf‐Enhanced Whale Optimization Algorithm to the Capacitated Vehicle Routing Problem

Journal of Advanced Transportation, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

The study presents a novel hybrid gray wolf and whale optimization algorithm (hGWOAM) for the capacitated vehicle routing problem (CVRP). By integrating enhanced (EWOA) optimizer (GWO) with tournament selection, opposition‐based learning, mutation techniques, hGWOAM enhances efficiency under capacity constraints. Computational evaluations demonstrate its superior performance, achieving lower percentage deviations (%dev) compared to existing algorithms across multiple case studies real‐world applications. In Case Study 1, achieved mean deviation than EWOA (0.89%), GWO (0.74%), SCA (0.59%), DA (1.63%), ALO (2.26%), MHPSO (1.85%), PSO (1.96%), DPGA (2.85%), SGA (4.14%). 2, outperformed (12.05%), (2.53%), (21.07%), (17.58%). application, it best %dev, surpassing (6.64%), (6.34%), (9.01%), (12.24%). These findings highlight hGWOAM’s potential optimizing logistics, reducing operational costs, minimizing environmental impact while also paving way future advancements in metaheuristic optimization.

Язык: Английский

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HybridGWOSPEA2ABC: a novel feature selection algorithm for gene expression data analysis and cancer classification

Computer Methods in Biomechanics & Biomedical Engineering, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Апрель 26, 2025

DNA micro-array technology has a remarkable impact on biological research, particularly in categorizing and diagnosing cancer studying gene features functions. With the availability of extensive collections cancer-related data, there been an increased focus developing optimized Machine Learning (ML) techniques for classification through pattern analysis identification specific genes type categorization. The relevant selection treating poses significant challenge, which requires efficient feature methods. This study introduces novel hybrid algorithm, selection, integrating Grey Wolf Optimizer (GWO), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Artificial Bee Colony (ABC). combination uses intelligence evolutionary computation to enhance solution diversity, convergence efficiency, exploration exploitation capabilities high-dimensional expression data. algorithm was compared with five bio-inspired algorithms using different classifiers various datasets validate its effectiveness selection. HybridGWOSPEA2ABC demonstrated superior performance identifying biomarkers conventional algorithms. Comparison benchmark shown approach's enhanced capability addressing challenges data advancing problem classification. hybridization enhances by maintaining efficiently converging optimal solutions, improving search space. provides better understanding promotes effective methodologies disease detection

Язык: Английский

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An automatic glaucoma grading method based on attention mechanism and EfficientNet-B3 network

PLoS ONE, Год журнала: 2024, Номер 19(8), С. e0296229 - e0296229

Опубликована: Авг. 16, 2024

Glaucoma infection is rapidly spreading globally and the number of glaucoma patients expected to exceed 110 million by 2040. Early identification detection particularly important as it can easily lead irreversible vision damage or even blindness if not treated with intervention in early stages. Deep learning has attracted much attention field computer been widely studied especially recognition diagnosis ophthalmic diseases. It challenging efficiently extract effective features for accurate grading a limited dataset. Currently, algorithms, 2D fundus images are mainly used automatically identify disease not, but do distinguish between late stages; however, clinical practice, treatment same, so more proceed achieve glaucoma. This study uses private dataset containing modal data, images, 3D-OCT scanner therein an triple classification (normal, early, moderately advanced) optimal performance on various measures. In view this, this paper proposes automatic method based mechanism EfficientNetB3 network. The network ResNet34 built fuse respectively, classification. proposed auto-classification minimizes feature redundancy while improving accuracy, incorporates two-branch model, which enables convolutional neural focus its main eye discard meaningless black background region image improve model. combined cross-entropy function achieves highest accuracy up 97.83%. Since ensures reliable decision-making screening, be second opinion tool doctors, greatly reduce missed misdiagnosis buy time patient’s treatment.

Язык: Английский

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Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component

Measurement, Год журнала: 2024, Номер 242, С. 116229 - 116229

Опубликована: Ноя. 14, 2024

Язык: Английский

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WITHDRAWN: LSAC-Net: A lightweight scale-aware CNN with densely connected focal modulation for retinal blood vessel segmentation

Heliyon, Год журнала: 2024, Номер unknown, С. e33515 - e33515

Опубликована: Июль 1, 2024

Язык: Английский

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