Improved sine cosine algorithm for large-scale optimization problems
Aiming at the shortcomings of the sine cosine algorithm (SCA) in solving the large-scale optimization problems, such as low accuracy, slow convergence speed, and being easy to fall into the dimension disaster, we propose an sine cosine algorithm with Lévy flight (SCAL). By using the element-by-eleme...
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| Format: | Article |
| Language: | English |
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Science Press (China Science Publishing & Media Ltd.)
2022-11-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
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| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2477 |
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| _version_ | 1850038232458199040 |
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| author | ZHANG Chao YANG Yi |
| author_facet | ZHANG Chao YANG Yi |
| author_sort | ZHANG Chao |
| collection | DOAJ |
| description | Aiming at the shortcomings of the sine cosine algorithm (SCA) in solving the large-scale optimization problems, such as low accuracy, slow convergence speed, and being easy to fall into the dimension disaster, we propose an sine cosine algorithm with Lévy flight (SCAL). By using the element-by-element multiplication of the Lévy flight distribution with the individual position vector of sine and cosine population, the characteristics and information of Lévy flight distribution are integrated into the individual information, so that it can possess the characteristic of random walk of Lévy flight and enhances the ability of local exploitation to escape from local extremum. A novel nonlinear parameter adjustment method based on spatial distance is adopted to balance the local exploitation and global exploration, which improves the convergence speed of the algorithm. On 14 classic test functions with dimensions of 100, 1 000 and 5 000 respectively, SCAL is compared with five swarm intelligence algorithms including SCA, flower pollination algorithm (FPA), particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and whale optimization algorithm (WOA). The experimental results indicate that SCAL has a significant advantage over the five swarm intelligence algorithms in terms of convergence accuracy, convergence speed and robustness. Compared with the improved wolf pack algorithm (IWPA), the improved flower pollination algorithm (IFPA), the improved whale optimization algorithm (IWOA), and the modified whale optimization algorithm (MWOA), which are suitable for solving large scale optimization problems, it is found that the overall optimization result of SCAL is better than the comparison algorithms and thus demonstrate that the proposed algorithm has the obvious advantages and competitiveness for solving large-scale optimization problems. |
| format | Article |
| id | doaj-art-4592dc14093441fc9fcb2fe0a62e7b47 |
| institution | DOAJ |
| issn | 1000-2618 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Science Press (China Science Publishing & Media Ltd.) |
| record_format | Article |
| series | Shenzhen Daxue xuebao. Ligong ban |
| spelling | doaj-art-4592dc14093441fc9fcb2fe0a62e7b472025-08-20T02:56:39ZengScience Press (China Science Publishing & Media Ltd.)Shenzhen Daxue xuebao. Ligong ban1000-26182022-11-0139668469210.3724/SP.J.1249.2022.066841000-2618（2022）06-0684-09Improved sine cosine algorithm for large-scale optimization problemsZHANG ChaoYANG YiAiming at the shortcomings of the sine cosine algorithm (SCA) in solving the large-scale optimization problems, such as low accuracy, slow convergence speed, and being easy to fall into the dimension disaster, we propose an sine cosine algorithm with Lévy flight (SCAL). By using the element-by-element multiplication of the Lévy flight distribution with the individual position vector of sine and cosine population, the characteristics and information of Lévy flight distribution are integrated into the individual information, so that it can possess the characteristic of random walk of Lévy flight and enhances the ability of local exploitation to escape from local extremum. A novel nonlinear parameter adjustment method based on spatial distance is adopted to balance the local exploitation and global exploration, which improves the convergence speed of the algorithm. On 14 classic test functions with dimensions of 100, 1 000 and 5 000 respectively, SCAL is compared with five swarm intelligence algorithms including SCA, flower pollination algorithm (FPA), particle swarm optimization (PSO) algorithm, sparrow search algorithm (SSA) and whale optimization algorithm (WOA). The experimental results indicate that SCAL has a significant advantage over the five swarm intelligence algorithms in terms of convergence accuracy, convergence speed and robustness. Compared with the improved wolf pack algorithm (IWPA), the improved flower pollination algorithm (IFPA), the improved whale optimization algorithm (IWOA), and the modified whale optimization algorithm (MWOA), which are suitable for solving large scale optimization problems, it is found that the overall optimization result of SCAL is better than the comparison algorithms and thus demonstrate that the proposed algorithm has the obvious advantages and competitiveness for solving large-scale optimization problems.https://journal.szu.edu.cn/en/#/digest?ArticleID=2477artificial intelligencesine cosine algorithmlarge-scale optimization problemslévy flightnonlinear parameter adjustment based on distanceconvergence speedconvergence accuracy |
| spellingShingle | ZHANG Chao YANG Yi Improved sine cosine algorithm for large-scale optimization problems Shenzhen Daxue xuebao. Ligong ban artificial intelligence sine cosine algorithm large-scale optimization problems lévy flight nonlinear parameter adjustment based on distance convergence speed convergence accuracy |
| title | Improved sine cosine algorithm for large-scale optimization problems |
| title_full | Improved sine cosine algorithm for large-scale optimization problems |
| title_fullStr | Improved sine cosine algorithm for large-scale optimization problems |
| title_full_unstemmed | Improved sine cosine algorithm for large-scale optimization problems |
| title_short | Improved sine cosine algorithm for large-scale optimization problems |
| title_sort | improved sine cosine algorithm for large scale optimization problems |
| topic | artificial intelligence sine cosine algorithm large-scale optimization problems lévy flight nonlinear parameter adjustment based on distance convergence speed convergence accuracy |
| url | https://journal.szu.edu.cn/en/#/digest?ArticleID=2477 |
| work_keys_str_mv | AT zhangchao improvedsinecosinealgorithmforlargescaleoptimizationproblems AT yangyi improvedsinecosinealgorithmforlargescaleoptimizationproblems |