An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning
The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based lea...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/23/3905 |
| _version_ | 1797463399691452416 |
|---|---|
| author | Qifa Wang Guanhua Cheng Peng Shao |
| author_facet | Qifa Wang Guanhua Cheng Peng Shao |
| author_sort | Qifa Wang |
| collection | DOAJ |
| description | The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors. |
| first_indexed | 2024-03-09T17:50:08Z |
| format | Article |
| id | doaj.art-308ba1fb237c4aad821133986432fd47 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:50:08Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-308ba1fb237c4aad821133986432fd472023-11-24T10:47:30ZengMDPI AGElectronics2079-92922022-11-011123390510.3390/electronics11233905An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based LearningQifa Wang0Guanhua Cheng1Peng Shao2School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, ChinaSchool of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, ChinaSchool of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, ChinaThe Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors.https://www.mdpi.com/2079-9292/11/23/3905optimizationswarm intelligent algorithmsbeetle antennae searchbeetle swarm optimizationopposition-based learning |
| spellingShingle | Qifa Wang Guanhua Cheng Peng Shao An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning Electronics optimization swarm intelligent algorithms beetle antennae search beetle swarm optimization opposition-based learning |
| title | An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning |
| title_full | An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning |
| title_fullStr | An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning |
| title_full_unstemmed | An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning |
| title_short | An Adaptive Beetle Swarm Optimization Algorithm with Novel Opposition-Based Learning |
| title_sort | adaptive beetle swarm optimization algorithm with novel opposition based learning |
| topic | optimization swarm intelligent algorithms beetle antennae search beetle swarm optimization opposition-based learning |
| url | https://www.mdpi.com/2079-9292/11/23/3905 |
| work_keys_str_mv | AT qifawang anadaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning AT guanhuacheng anadaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning AT pengshao anadaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning AT qifawang adaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning AT guanhuacheng adaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning AT pengshao adaptivebeetleswarmoptimizationalgorithmwithnoveloppositionbasedlearning |