A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization
The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for i...
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8949461/ |
| _version_ | 1818577996501483520 |
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| author | Xiaofeng Yue Hongbo Zhang Haiyue Yu |
| author_facet | Xiaofeng Yue Hongbo Zhang Haiyue Yu |
| author_sort | Xiaofeng Yue |
| collection | DOAJ |
| description | The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm. |
| first_indexed | 2024-12-16T06:38:47Z |
| format | Article |
| id | doaj.art-1d957e31b2314a73a8930a3e9c790ffc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T06:38:47Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1d957e31b2314a73a8930a3e9c790ffc2022-12-21T22:40:44ZengIEEEIEEE Access2169-35362020-01-0185928596010.1109/ACCESS.2019.29636798949461A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global OptimizationXiaofeng Yue0https://orcid.org/0000-0003-0809-8949Hongbo Zhang1https://orcid.org/0000-0003-1926-9571Haiyue Yu2https://orcid.org/0000-0002-3543-5886School of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaSchool of Mechatronic Engineering, Changchun University of Technology, Changchun, ChinaThe grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm.https://ieeexplore.ieee.org/document/8949461/Grasshopper optimization algorithminvasive weed optimizationgrouping strategyrandom walk strategyglobal optimization |
| spellingShingle | Xiaofeng Yue Hongbo Zhang Haiyue Yu A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization IEEE Access Grasshopper optimization algorithm invasive weed optimization grouping strategy random walk strategy global optimization |
| title | A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization |
| title_full | A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization |
| title_fullStr | A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization |
| title_full_unstemmed | A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization |
| title_short | A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization |
| title_sort | hybrid grasshopper optimization algorithm with invasive weed for global optimization |
| topic | Grasshopper optimization algorithm invasive weed optimization grouping strategy random walk strategy global optimization |
| url | https://ieeexplore.ieee.org/document/8949461/ |
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