A Comparative Analysis Of African Vultures Optimization Algorithm With Current Metaheuristics

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Journal Year: 2025, Volume and Issue: 8(1), P. 325 - 352

Published: Jan. 15, 2025

With the increasing complexity of optimization problems, new metaheuristic algorithms are being developed. These show their success by exhibiting superior performances on different problems. In this paper, performance 4 recently proposed algorithms, namely Artificial Hummingbird Algorithm (AHA), African Vultures Optimization (AVOA), Crayfish (COA) and Marine Predators (MPA) 26 test functions compared. As a result comparisons, it was observed that outperformed each other with very small differences functions. At same time, comparison results were evaluated t-test statistical test. AVOA has shown better or comparable to recent metaheuristics in evaluating quality solutions for several It is aimed use problems future research.

Language: Английский

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The Application of the SubChain Salp Swarm Algorithm in the Less-Than-Truckload Freight Matching Problem

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4436 - 4436

Published: April 17, 2025

The less-than-truckload (LTL) freight problem is a general pain point in logistics applications. Its challenge resides the fact that these loads cannot be shipped timely manner due to their relatively small volumes. Traditional LTL matching methods are challenged by delays updating logistic information and higher distribution costs. In order solve challenges, we developed novel SubChain Salp Swarm Algorithm (SSSA) improving traditional with utilization of operation. Our method aims find optimal strategy for maintaining balance between lower operating costs customer satisfaction. SSSA combines multiple disconnected points separate individual chains local optima obtain better convergence results final decision. We have compared our mainstream metaheuristic algorithms using datasets from road company Hangzhou, demonstrate converges faster than other has variance. solves limitation observed optimization improves service relation transportation issue.

Language: Английский

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Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm: Improving Extreme Learning Machine Diagnostics in Breast Cancer

Mathematics, Journal Year: 2024, Volume and Issue: 12(17), P. 2641 - 2641

Published: Aug. 26, 2024

Extreme learning machines (ELMs), single hidden-layer feedforward neural networks, are renowned for their speed and efficiency in classification regression tasks. However, generalization ability is often undermined by the random generation of hidden layer weights biases. To address this issue, paper introduces a Hierarchical Learning-based Chaotic Crayfish Optimization Algorithm (HLCCOA) aimed at enhancing ELMs. Initially, to resolve problems slow search premature convergence typical traditional crayfish optimization algorithms (COAs), HLCCOA utilizes chaotic sequences population position initialization. The ergodicity chaos leveraged boost diversity, laying groundwork effective global efforts. Additionally, hierarchical mechanism encourages under-performing individuals engage extensive cross-layer enhanced exploration, while top performers directly learn from elite highest improve local exploitation abilities. Rigorous testing with CEC2019 CEC2022 suites shows HLCCOA’s superiority over both original COA nine heuristic algorithms. Ultimately, HLCCOA-optimized extreme machine model, HLCCOA-ELM, exhibits superior performance reported benchmark models terms accuracy, sensitivity, specificity UCI breast cancer diagnosis, underscoring practicality robustness, as well HLCCOA-ELM’s commendable performance.

Language: Английский

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Utilizing computer vision and deep learning to detect and monitor insects in real time by analyzing camera trap images

Natural and Engineering Sciences, Journal Year: 2024, Volume and Issue: 9(2), P. 280 - 292

Published: Oct. 30, 2024

Insect monitoring techniques are often labor-intensive and need significant resources for identifying species after manual field traps. traps usually maintained every week, leading to a low temporal accuracy of information collected that impedes ecological analysis. This study introduces handheld computer vision device attract detect real insects. The research explicitly proposes categorizing by imaging live drawn camera trapping. An Automatic Moth Trapping (AMT) equipped with light elemnets was developed draw observe insects throughout twilight nocturnal periods. Classification Counting (MCC) utilizes Computer Vision (CV) Deep Learning (DL) evaluation pictures monitors. It enumerates insect populations while moth species. Over 48 nights, more than 250k photos were captured, averaging 5.6k daily. A tailored Convolutional Neural Networks (CNN) on 2000 labeled across eight distinct categories. suggested method methodology have shown encouraging outcomes as an economical option automated surveillance

Language: Английский

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