Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator
In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad de...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9874816/ |
| _version_ | 1811267045933711360 |
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| author | Renju Liu Tianlei Wang Jing Zhou Xiaoxi Hao Ying Xu Jiongzhi Qiu |
| author_facet | Renju Liu Tianlei Wang Jing Zhou Xiaoxi Hao Ying Xu Jiongzhi Qiu |
| author_sort | Renju Liu |
| collection | DOAJ |
| description | In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions. |
| first_indexed | 2024-04-12T20:55:22Z |
| format | Article |
| id | doaj.art-0da201e4b4f144c389162a8021f6b5e9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T20:55:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0da201e4b4f144c389162a8021f6b5e92022-12-22T03:17:00ZengIEEEIEEE Access2169-35362022-01-0110951979521810.1109/ACCESS.2022.32038139874816Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution OperatorRenju Liu0https://orcid.org/0000-0002-1232-0453Tianlei Wang1https://orcid.org/0000-0002-6983-0788Jing Zhou2https://orcid.org/0000-0002-2249-852XXiaoxi Hao3Ying Xu4https://orcid.org/0000-0003-0597-8537Jiongzhi Qiu5https://orcid.org/0000-0002-5944-2079Department of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaDepartment of Intelligent Manufacturing, Wuyi University, Jiangmen, ChinaIn this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.https://ieeexplore.ieee.org/document/9874816/African vulture optimization algorithmbenchmark functiondifferential evolutionquasi-oppositional learningswarm intelligence algorithm |
| spellingShingle | Renju Liu Tianlei Wang Jing Zhou Xiaoxi Hao Ying Xu Jiongzhi Qiu Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator IEEE Access African vulture optimization algorithm benchmark function differential evolution quasi-oppositional learning swarm intelligence algorithm |
| title | Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator |
| title_full | Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator |
| title_fullStr | Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator |
| title_full_unstemmed | Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator |
| title_short | Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator |
| title_sort | improved african vulture optimization algorithm based on quasi oppositional differential evolution operator |
| topic | African vulture optimization algorithm benchmark function differential evolution quasi-oppositional learning swarm intelligence algorithm |
| url | https://ieeexplore.ieee.org/document/9874816/ |
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