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Adegboye OR, Feda AK, Agyekum EB, Mbasso WF, Kamel S. Towards greener futures: SVR-based CO 2 prediction model boosted by SCMSSA algorithm. Heliyon 2024; 10:e31766. [PMID: 38845912 PMCID: PMC11154620 DOI: 10.1016/j.heliyon.2024.e31766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
This research presents the utilization of an enhanced Sine cosine perturbation with Chaotic perturbation and Mirror imaging strategy-based Salp Swarm Algorithm (SCMSSA), which incorporates three improvement mechanisms, to enhance the convergence accuracy and speed of the optimization algorithm. The study assesses the SCMSSA algorithm's performance against other optimization algorithms using six test functions to show the efficacy of the enhancement strategies. Furthermore, its efficacy in improving Support Vector Regression (SVR) models for CO2 prediction is assessed. The results reveal that the SVR-SCMSSA hybrid model surpasses other hybrid models and standard SVR in terms of training and prediction accuracy by obtaining 95 % accuracy. Its swift convergence, precision, and resistance to local optima position make it an excellent choice for addressing complex problems such as CO2 prediction, with critical implications for sustainability efforts. Moreover, feature importance analysis by SVR-SCMSSA offers valuable insights into the key contributors to CO2 prediction in the dataset, emphasizing the significance and impact of factors such as fossil fuel, Biomass, and Wood as major contributors to CO2 emission. The research suggests the adoption of the SVR-SCMSSA hybrid model for more accurate and reliable CO2 prediction to researchers and policymakers, which is essential for environmental sustainability and climate change mitigation.
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Affiliation(s)
| | - Afi Kekeli Feda
- Advanced Research Centre, European University of Lefke, Mersin-10, Turkey
| | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Ekaterinburg, 620002, Russia
| | - Wulfran Fendzi Mbasso
- Laboratory of Technology and Applied Sciences, University Institute of Technology, University of Douala, PO Box: 8698 Douala, Cameroon
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
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Adegboye OR, Ülker ED, Feda AK, Agyekum EB, Fendzi Mbasso W, Kamel S. Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO). Heliyon 2024; 10:e31850. [PMID: 38882359 PMCID: PMC11176760 DOI: 10.1016/j.heliyon.2024.e31850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/01/2024] [Accepted: 05/22/2024] [Indexed: 06/18/2024] Open
Abstract
This study introduces the Worst Moth Disruption Strategy (WMFO) to enhance the Moth Fly Optimization (MFO) algorithm, specifically addressing challenges related to population stagnation and low diversity. The WMFO aims to prevent local trapping of moths, fostering improved global search capabilities. Demonstrating a remarkable efficiency of 66.6 %, WMFO outperforms the MFO on CEC15 benchmark test functions. The Friedman and Wilcoxon tests further confirm WMFO's superiority over state-of-the-art algorithms. Introducing a hybrid model, WMFO-MLP, combining WMFO with a Multi-Layer Perceptron (MLP), facilitates effective parameter tuning for carbon emission prediction, achieving an outstanding total accuracy of 97.8 %. Comparative analysis indicates that the MLP-WMFO model surpasses alternative techniques in precision, reliability, and efficiency. Feature importance analysis reveals that variables such as Oil Efficiency and Economic Growth significantly impact MLP-WMFO's predictive power, contributing up to 40 %. Additionally, Gas Efficiency, Renewable Energy, Financial Risk, and Political Risk explain 26.5 %, 13.6 %, 8 %, and 6.5 %, respectively. Finally, WMFO-MLP performance offers advancements in optimization and predictive modeling with practical applications in carbon emission prediction.
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Affiliation(s)
| | - Ezgi Deniz Ülker
- Computer Engineering, European University of Lefke, Mersin-10, Turkey
| | - Afi Kekeli Feda
- Advanced Research Centre, European University of Lefke, Northern Cyprus, TR-10, Mersin, Turkey
| | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University named after the first President of Russia Boris Yeltsin, 620002, 19 Mira Street, Ekaterinburg, Russia
| | - Wulfran Fendzi Mbasso
- Technology and Applied Sciences Laboratory, UIT of Douala, P.O. Box 8689, Douala, University of Douala, Cameroon
| | - Salah Kamel
- Electrical Engineering Department, Faculty of Engineering, Aswan University, 81542, Aswan, Egypt
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Adegboye OR, Feda AK, Ojekemi OS, Agyekum EB, Hussien AG, Kamel S. Chaotic opposition learning with mirror reflection and worst individual disturbance grey wolf optimizer for continuous global numerical optimization. Sci Rep 2024; 14:4660. [PMID: 38409189 PMCID: PMC10897155 DOI: 10.1038/s41598-024-55040-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 02/20/2024] [Indexed: 02/28/2024] Open
Abstract
The effective meta-heuristic technique known as the grey wolf optimizer (GWO) has shown its proficiency. However, due to its reliance on the alpha wolf for guiding the position updates of search agents, the risk of being trapped in a local optimal solution is notable. Furthermore, during stagnation, the convergence of other search wolves towards this alpha wolf results in a lack of diversity within the population. Hence, this research introduces an enhanced version of the GWO algorithm designed to tackle numerical optimization challenges. The enhanced GWO incorporates innovative approaches such as Chaotic Opposition Learning (COL), Mirror Reflection Strategy (MRS), and Worst Individual Disturbance (WID), and it's called CMWGWO. MRS, in particular, empowers certain wolves to extend their exploration range, thus enhancing the global search capability. By employing COL, diversification is intensified, leading to reduced solution stagnation, improved search precision, and an overall boost in accuracy. The integration of WID fosters more effective information exchange between the least and most successful wolves, facilitating a successful exit from local optima and significantly enhancing exploration potential. To validate the superiority of CMWGWO, a comprehensive evaluation is conducted. A wide array of 23 benchmark functions, spanning dimensions from 30 to 500, ten CEC19 functions, and three engineering problems are used for experimentation. The empirical findings vividly demonstrate that CMWGWO surpasses the original GWO in terms of convergence accuracy and robust optimization capabilities.
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Affiliation(s)
| | - Afi Kekeli Feda
- Management Information System Department, European University of Lefke, Mersin-10, Turkey
| | | | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Yekaterinburg, Russia, 620002
| | - Abdelazim G Hussien
- Department of Computer and Information Science, Linköping University, Linköping, Sweden.
- Faculty of Science, Fayoum University, El Faiyûm, Egypt.
- Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan.
- MEU Research Unit, Middle East University, Amman, 11831, Jordan.
| | - Salah Kamel
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
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Feda AK, Adegboye M, Adegboye OR, Agyekum EB, Fendzi Mbasso W, Kamel S. S-shaped grey wolf optimizer-based FOX algorithm for feature selection. Heliyon 2024; 10:e24192. [PMID: 38293420 PMCID: PMC10825485 DOI: 10.1016/j.heliyon.2024.e24192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/09/2023] [Accepted: 01/04/2024] [Indexed: 02/01/2024] Open
Abstract
The FOX algorithm is a recently developed metaheuristic approach inspired by the behavior of foxes in their natural habitat. While the FOX algorithm exhibits commendable performance, its basic version, in complex problem scenarios, may become trapped in local optima, failing to identify the optimal solution due to its weak exploitation capabilities. This research addresses a high-dimensional feature selection problem. In feature selection, the most informative features are retained while discarding irrelevant ones. An enhanced version of the FOX algorithm is proposed, aiming to mitigate its drawbacks in feature selection. The improved approach referred to as S-shaped Grey Wolf Optimizer-based FOX (FOX-GWO), which focuses on augmenting the local search capabilities of the FOX algorithm via the integration of GWO. Additionally, the introduction of an S-shaped transfer function enables the population to explore both binary options throughout the search process. Through a series of experiments on 18 datasets with varying dimensions, FOX-GWO outperforms in 83.33 % of datasets for average accuracy, 61.11 % for reduced feature dimensionality, and 72.22 % for average fitness value across the 18 datasets. Meaning it efficiently explores high-dimensional spaces. These findings highlight its practical value and potential to advance feature selection in complex data analysis, enhancing model prediction accuracy.
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Affiliation(s)
- Afi Kekeli Feda
- Management Information System Department, European University of Lefke, Mersin, 10, Turkey
| | | | | | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University named after the first President of Russia Boris Yeltsin, 620002, 19 Mira Street, Ekaterinburg, Russia
| | - Wulfran Fendzi Mbasso
- Laboratory of Technology and Applied Sciences, University Institute of Technology, University of Douala, PO Box: 8698, Douala, Cameroon
| | - Salah Kamel
- Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
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Adegboye OR, Feda AK, Ojekemi OR, Agyekum EB, Khan B, Kamel S. DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance. Sci Rep 2024; 14:1491. [PMID: 38233528 PMCID: PMC10794415 DOI: 10.1038/s41598-023-50910-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/27/2023] [Indexed: 01/19/2024] Open
Abstract
This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.
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Affiliation(s)
| | - Afi Kekeli Feda
- Management Information System Department, European University of Lefke, Mersin-10, Turkey
| | | | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris Yeltsin, 19 Mira Street, Ekaterinburg, Russia, 620002
| | - Baseem Khan
- Department of Electrical and Computer Engineering, Hawassa University, Hawassa, Ethiopia.
| | - Salah Kamel
- Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt
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Adegboye OR, Feda AK, Ishaya MM, Agyekum EB, Kim KC, Mbasso WF, Kamel S. Antenna S-parameter optimization based on golden sine mechanism based honey badger algorithm with tent chaos. Heliyon 2023; 9:e21596. [PMID: 38034692 PMCID: PMC10682539 DOI: 10.1016/j.heliyon.2023.e21596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 10/22/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
This work proposed a new method to optimize the antenna S-parameter using a Golden Sine mechanism-based Honey Badger Algorithm that employs Tent chaos (GST-HBA). The Honey Badger Algorithm (HBA) is a promising optimization method that similar to other metaheuristic algorithms, is prone to premature convergence and lacks diversity in the population. The Honey Badger Algorithm is inspired by the behavior of honey badgers who use their sense of smell and honeyguide birds to move toward the honeycomb. Our proposed approach aims to improve the performance of HBA and enhance the accuracy of the optimization process for antenna S-parameter optimization. The approach we propose in this study leverages the strengths of both tent chaos and the golden sine mechanism to achieve fast convergence, population diversity, and a good tradeoff between exploitation and exploration. We begin by testing our approach on 20 standard benchmark functions, and then we apply it to a test suite of 8 S-parameter functions. We perform tests comparing the outcomes to those of other optimization algorithms, the result shows that the suggested algorithm is superior.
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Affiliation(s)
| | - Afi Kekeli Feda
- Management Information System Department, European University of Lefke, Mersin, 10, Turkey
| | - Meshack Magaji Ishaya
- Electrical and Electronics Engineering Department, Cyprus International University, Mersin, 10, Turkey
| | - Ephraim Bonah Agyekum
- Department of Nuclear and Renewable Energy, Ural Federal University Named After the First President of Russia Boris, 19 Mira Street, Yeltsin, Ekaterinburg, 620002, Russia
| | - Ki-Chai Kim
- Department of Electrical Engineering, Yeungnam University, Gyeongsan, 38541, South Korea
| | - Wulfran Fendzi Mbasso
- Laboratory of Technology and Applied Sciences, University Institute of Technology, University of Douala, PO Box: 8698, Douala, Cameroon
| | - Salah Kamel
- Electrical Engineering Department, Faculty of Engineering, Aswan University, 81542, Aswan, Egypt
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Xiong Y, Zou Z, Cheng J. Cuckoo search algorithm based on cloud model and its application. Sci Rep 2023; 13:10098. [PMID: 37344537 DOI: 10.1038/s41598-023-37326-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/20/2023] [Indexed: 06/23/2023] Open
Abstract
Cuckoo search algorithm is an efficient random search method for numerical optimization. However, it is very sensitive to the setting of the step size factor. To address this issue, a new cuckoo search algorithm based on cloud model is developed to dynamically configure the step size factor. More specifically, the idea of giving consideration to both fuzziness and randomness of cloud model is innovatively introduced into cuckoo search algorithm, and the appropriate step size factor can be determined according to the membership degree and an exponential function, so as to realize the adaptive adjustment of the control parameter. After that, simulation experiments are conducted on 25 benchmark functions with different dimensions and two chaotic time series prediction problems to comprehensively evaluate the superiority of the proposed algorithm. Numerical results demonstrate that the developed method is more competitive than the other five CS and several non-CS algorithms.
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Affiliation(s)
- Yan Xiong
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China
| | - Ziming Zou
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China
| | - Jiatang Cheng
- College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China.
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