A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification Problem

Ali, Mona A. S.; P., Fathimathul Rajeena P.; AbdElminaam, Diaa Salama

doi:10.3390/math10152675

Cited by 27 publications

(5 citation statements)

References 134 publications

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“…On the other hand, some recent algorithms, such as AHA and GTO, have been used in similar problems where remarkable results were obtained [60]- [62], while others, such as the SFS and POA algorithms, stand out for their good performance in benchmark functions.…”

Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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Musculoskeletal disorders of the foot are a common complaint in the population. It has been found a flatfoot prevalence of 13.6% in young adults and a prevalence of 26.62% in adults between 42 and 91 years. Different non-invasive techniques can identify the type of foot by analyzing the soles of the feet, such as the Chippaux-Smirak index (CSI). Although CSI is a non-invasive technique, it is performed manually, and the intervention of an expert is necessary to give a clinical opinion. The use of automatic systems is an alternative. This article introduces a machine learning-based tool that permits the identification of foot types. The proposal employs a wrapper feature selection mechanism to select the subset of features that improves the classification. This task is considered from an optimization perspective, and the optimal subset is chosen using metaheuristic algorithms. Eight algorithms used in the optimization are compared, and an increase in the Accuracy of the K-nearest neighbors (KNN) classifier is observed from 73.5% to 94.7%. Of the 39 total features proposed in the dataset, only 10 features are considered significant. The significance of the characteristics implies that they have an effect on the morphology of the foot. If they are considered in treatments to minimize this disease, it can reduce their development costs.

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Section: Metaheuristicsmentioning

confidence: 99%

Wrapper-Based Feature Selection to Classify Flatfoot Disease

Miguel-Andrés,

Ramos-Frutos,

Sharawi

et al. 2024

IEEE Access

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“…By using random reversal learning to generate a reversed population, the search space of the population is expanded, providing more opportunities to discover potential optimal individuals and avoiding the problem of easily getting trapped in local optima during the algorithm's evolution. The random reversal learning strategy enriches population diversity and effectively improves the exploration capability of the DBO algorithm, which is weaker than its exploitation capability and prone to premature convergence or local stagnation [41]. The specific formula is as follows:…”

Section: Random Backward Learning Strategymentioning

confidence: 99%

Enhancing Swarm Intelligence for Obstacle Avoidance with Multi-Strategy and Improved Dung Beetle Optimization Algorithm in Mobile Robot Navigation

Li,

Liu,

Shao

et al. 2023

Electronics

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The Dung Beetle Optimization (DBO) algorithm is a powerful metaheuristic algorithm that is widely used for optimization problems. However, the DBO algorithm has limitations in balancing global exploration and local exploitation capabilities, often leading to getting stuck in local optima. To overcome these limitations and address global optimization problems, this study introduces the Multi-Strategy and Improved DBO (MSIDBO) Algorithm. The MSIDBO algorithm incorporates several advanced computational techniques to enhance its performance. Firstly, it introduces a random reverse learning strategy to improve population diversity and mitigate early convergence or local stagnation issues present in the DBO algorithm. Additionally, a fitness-distance balancing strategy is employed to better manage the trade-off between diversity and convergence within the population. Furthermore, the algorithm utilizes a spiral foraging strategy to enhance precision, promote strong exploratory capabilities, and prevent being trapped in local optima. To further enhance the global search ability and particle utilization of the MSIDBO algorithm, it combines the Optimal Dimension-Wise Gaussian Mutation strategy. By minimizing premature convergence, population diversity is increased, and the convergence of the algorithm is accelerated. This expansion of the search space reduces the likelihood of being trapped in local optima during the evolutionary process. To demonstrate the effectiveness of the MSIDBO algorithm, extensive experiments are conducted using benchmark test functions, comparing its performance against other well-known metaheuristic algorithms. The results highlight the feasibility and superiority of MSIDBO in solving optimization problems. Moreover, the MSIDBO algorithm is applied to path planning simulation experiments to showcase its practical application potential. A comparison with the DBO algorithm shows that MSIDBO generates shorter and faster paths, effectively addressing real-world application problems.

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“…In the original publication [1], there was an error regarding the affiliation(s) for **Diaa Salama Abd Elminaam**. In addition to affiliation And the author also wants to change the email information to: diaa.salama@fci.bu.edu.eg or diaa.salama@miuegypt.edu.eg or ds_desert@yahoo.com.…”

Section: Additional Affiliation(s)mentioning

confidence: 99%