Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems
Abstract The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-10-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00350-2 |
| _version_ | 1797647176352923648 |
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| author | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen |
| author_facet | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen |
| author_sort | Vu Hong Son Pham |
| collection | DOAJ |
| description | Abstract The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems. |
| first_indexed | 2024-03-11T15:12:32Z |
| format | Article |
| id | doaj.art-29b0e9288af94d9db36440608cba7c60 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-03-11T15:12:32Z |
| publishDate | 2023-10-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-29b0e9288af94d9db36440608cba7c602023-10-29T12:36:13ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-10-0116112510.1007/s44196-023-00350-2Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization ProblemsVu Hong Son Pham0Nghiep Trinh Nguyen Dang1Van Nam Nguyen2Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM)Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM)Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University (VNU-HCM)Abstract The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.https://doi.org/10.1007/s44196-023-00350-2Evolutionary algorithmStochastic optimizationSine cosine algorithmRoulette wheel selectionOpposition-based learning |
| spellingShingle | Vu Hong Son Pham Nghiep Trinh Nguyen Dang Van Nam Nguyen Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems International Journal of Computational Intelligence Systems Evolutionary algorithm Stochastic optimization Sine cosine algorithm Roulette wheel selection Opposition-based learning |
| title | Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems |
| title_full | Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems |
| title_fullStr | Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems |
| title_full_unstemmed | Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems |
| title_short | Hybrid Sine Cosine Algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems |
| title_sort | hybrid sine cosine algorithm with integrated roulette wheel selection and opposition based learning for engineering optimization problems |
| topic | Evolutionary algorithm Stochastic optimization Sine cosine algorithm Roulette wheel selection Opposition-based learning |
| url | https://doi.org/10.1007/s44196-023-00350-2 |
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