Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems
The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high comput...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/945 |
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| author | Muhammad Kamran Khan Muhammad Hamza Zafar Saad Rashid Majad Mansoor Syed Kumayl Raza Moosavi Filippo Sanfilippo |
| author_facet | Muhammad Kamran Khan Muhammad Hamza Zafar Saad Rashid Majad Mansoor Syed Kumayl Raza Moosavi Filippo Sanfilippo |
| author_sort | Muhammad Kamran Khan |
| collection | DOAJ |
| description | The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique. |
| first_indexed | 2024-03-09T13:43:45Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:43:45Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e25a2cc496fa4f7f95d85e918b5f9b3d2023-11-30T21:03:57ZengMDPI AGApplied Sciences2076-34172023-01-0113294510.3390/app13020945Improved Reptile Search Optimization Algorithm: Application on Regression and Classification ProblemsMuhammad Kamran Khan0Muhammad Hamza Zafar1Saad Rashid2Majad Mansoor3Syed Kumayl Raza Moosavi4Filippo Sanfilippo5Faculty of Engineering Sciences, Islamabad Campus, Hamdard University, Islamabad 44000, PakistanDepartment of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, PakistanFaculty of Engineering Sciences, Islamabad Campus, Hamdard University, Islamabad 44000, PakistanDepartment of Automation, University of Science and Technology of China, Hefei 230027, ChinaSchool of Electrical and Electronics Engineering, National University of Sciences and Technology, Islamabad 44000, PakistanDepartment of Engineering Sciences, University of Agder (UiA), NO-4879 Grimstad, NorwayThe reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.https://www.mdpi.com/2076-3417/13/2/945metaheuristic optimizationneural networkimproved reptile search algorithmmulti-layer perceptronradial basis function network |
| spellingShingle | Muhammad Kamran Khan Muhammad Hamza Zafar Saad Rashid Majad Mansoor Syed Kumayl Raza Moosavi Filippo Sanfilippo Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems Applied Sciences metaheuristic optimization neural network improved reptile search algorithm multi-layer perceptron radial basis function network |
| title | Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems |
| title_full | Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems |
| title_fullStr | Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems |
| title_full_unstemmed | Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems |
| title_short | Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems |
| title_sort | improved reptile search optimization algorithm application on regression and classification problems |
| topic | metaheuristic optimization neural network improved reptile search algorithm multi-layer perceptron radial basis function network |
| url | https://www.mdpi.com/2076-3417/13/2/945 |
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