Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism
Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/8/1303 |
| _version_ | 1797445149879435264 |
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| author | Wei Li Haonan Luo Lei Wang Qiaoyong Jiang Qingzheng Xu |
| author_facet | Wei Li Haonan Luo Lei Wang Qiaoyong Jiang Qingzheng Xu |
| author_sort | Wei Li |
| collection | DOAJ |
| description | Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms. |
| first_indexed | 2024-03-09T13:21:34Z |
| format | Article |
| id | doaj.art-9e104471626041769fa21e37180c25ec |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T13:21:34Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-9e104471626041769fa21e37180c25ec2023-11-30T21:29:34ZengMDPI AGMathematics2227-73902022-04-01108130310.3390/math10081303Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning MechanismWei Li0Haonan Luo1Lei Wang2Qiaoyong Jiang3Qingzheng Xu4School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Information and Communication, National University of Defense Technology, Wuhan 430035, ChinaBrain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms.https://www.mdpi.com/2227-7390/10/8/1303brain storm optimization algorithmNelder–Meadelite learningopposition based learning |
| spellingShingle | Wei Li Haonan Luo Lei Wang Qiaoyong Jiang Qingzheng Xu Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism Mathematics brain storm optimization algorithm Nelder–Mead elite learning opposition based learning |
| title | Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism |
| title_full | Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism |
| title_fullStr | Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism |
| title_full_unstemmed | Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism |
| title_short | Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism |
| title_sort | enhanced brain storm optimization algorithm based on modified nelder mead and elite learning mechanism |
| topic | brain storm optimization algorithm Nelder–Mead elite learning opposition based learning |
| url | https://www.mdpi.com/2227-7390/10/8/1303 |
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