Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator
The grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8961985/ |
| _version_ | 1818920823888543744 |
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| author | S. S. Guo J. S. Wang W. Xie M. W. Guo L. F. Zhu |
| author_facet | S. S. Guo J. S. Wang W. Xie M. W. Guo L. F. Zhu |
| author_sort | S. S. Guo |
| collection | DOAJ |
| description | The grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process of GOA to improve the grasshopper's global exploration and avoid falling into local optimum in advance. At the same time, a pigeon search operator-landmark operator is introduced to improve and balance the algorithm's exploration and development capabilities. In order to verify the validity of the improved algorithm, this paper will adopts the gravity search operator and a deterrent landmark operator hybrid grasshoppers algorithm (HGOA) with basic grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine and cosine algorithm (SCA), moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), and bat algorithm (BA) to optimize 28 test functions. And the analysis and comparison of the obtained statistical data results finally show that the proposed improved grasshopper algorithm has better optimization ability. |
| first_indexed | 2024-12-20T01:27:53Z |
| format | Article |
| id | doaj.art-f78dd07ee5314b8d8615a0ea190394dd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:27:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f78dd07ee5314b8d8615a0ea190394dd2022-12-21T19:58:12ZengIEEEIEEE Access2169-35362020-01-018222032222410.1109/ACCESS.2020.29673998961985Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark OperatorS. S. Guo0https://orcid.org/0000-0003-1883-9958J. S. Wang1https://orcid.org/0000-0002-8853-1927W. Xie2M. W. Guo3L. F. Zhu4School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaThe grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the behavior of grasshopper groups. Aiming at the shortcomings of poor development ability and low convergence accuracy of GOA, this paper introduces the gravity search operator into the optimization process of GOA to improve the grasshopper's global exploration and avoid falling into local optimum in advance. At the same time, a pigeon search operator-landmark operator is introduced to improve and balance the algorithm's exploration and development capabilities. In order to verify the validity of the improved algorithm, this paper will adopts the gravity search operator and a deterrent landmark operator hybrid grasshoppers algorithm (HGOA) with basic grasshopper algorithm (GOA), particle swarm optimization (PSO) algorithm, sine and cosine algorithm (SCA), moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), and bat algorithm (BA) to optimize 28 test functions. And the analysis and comparison of the obtained statistical data results finally show that the proposed improved grasshopper algorithm has better optimization ability.https://ieeexplore.ieee.org/document/8961985/Grasshopper algorithmgravity search operatorPigeon landmark operatorfunction optimization |
| spellingShingle | S. S. Guo J. S. Wang W. Xie M. W. Guo L. F. Zhu Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator IEEE Access Grasshopper algorithm gravity search operator Pigeon landmark operator function optimization |
| title | Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator |
| title_full | Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator |
| title_fullStr | Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator |
| title_full_unstemmed | Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator |
| title_short | Improved Grasshopper Algorithm Based on Gravity Search Operator and Pigeon Colony Landmark Operator |
| title_sort | improved grasshopper algorithm based on gravity search operator and pigeon colony landmark operator |
| topic | Grasshopper algorithm gravity search operator Pigeon landmark operator function optimization |
| url | https://ieeexplore.ieee.org/document/8961985/ |
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