An Improved Grasshopper Optimizer for Global Tasks
The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper in...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4873501 |
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| author | Hanfeng Zhou Zewei Ding Hongxin Peng Zitao Tang Guoxi Liang Huiling Chen Chao Ma Mingjing Wang |
| author_facet | Hanfeng Zhou Zewei Ding Hongxin Peng Zitao Tang Guoxi Liang Huiling Chen Chao Ma Mingjing Wang |
| author_sort | Hanfeng Zhou |
| collection | DOAJ |
| description | The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems. |
| format | Article |
| id | doaj-art-dd72bced7b1e4650bc92557760f170f9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-dd72bced7b1e4650bc92557760f170f92025-02-03T06:04:37ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/48735014873501An Improved Grasshopper Optimizer for Global TasksHanfeng Zhou0Zewei Ding1Hongxin Peng2Zitao Tang3Guoxi Liang4Huiling Chen5Chao Ma6Mingjing Wang7College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaDepartment of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, ChinaSchool of Digital Media, Shenzhen Institute of Information Technology, Shenzhen 518172, ChinaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamThe grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.http://dx.doi.org/10.1155/2020/4873501 |
| spellingShingle | Hanfeng Zhou Zewei Ding Hongxin Peng Zitao Tang Guoxi Liang Huiling Chen Chao Ma Mingjing Wang An Improved Grasshopper Optimizer for Global Tasks Complexity |
| title | An Improved Grasshopper Optimizer for Global Tasks |
| title_full | An Improved Grasshopper Optimizer for Global Tasks |
| title_fullStr | An Improved Grasshopper Optimizer for Global Tasks |
| title_full_unstemmed | An Improved Grasshopper Optimizer for Global Tasks |
| title_short | An Improved Grasshopper Optimizer for Global Tasks |
| title_sort | improved grasshopper optimizer for global tasks |
| url | http://dx.doi.org/10.1155/2020/4873501 |
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