Simplex Search-Based Brain Storm Optimization
Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much mo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8546742/ |
| _version_ | 1819163048171012096 |
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| author | Wei Chen Yingying Cao Shi Cheng Yifei Sun Qunfeng Liu Yun Li |
| author_facet | Wei Chen Yingying Cao Shi Cheng Yifei Sun Qunfeng Liu Yun Li |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder-Mead Simplex method is adopted in BSO. Through combining BSO's exploration ability and NMS's exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems. |
| first_indexed | 2024-12-22T17:37:56Z |
| format | Article |
| id | doaj.art-aa746f6ea1c24010a2c0b34d4161bac6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:37:56Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aa746f6ea1c24010a2c0b34d4161bac62022-12-21T18:18:29ZengIEEEIEEE Access2169-35362018-01-016759977600610.1109/ACCESS.2018.28835068546742Simplex Search-Based Brain Storm OptimizationWei Chen0Yingying Cao1Shi Cheng2https://orcid.org/0000-0002-5129-995XYifei Sun3Qunfeng Liu4https://orcid.org/0000-0002-6286-941XYun Li5School of Computer Science and Network Security, Dongguan University of Technology, Dongguan, ChinaSchool of Computer Science and Network Security, Dongguan University of Technology, Dongguan, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an, ChinaKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, ChinaSchool of Computer Science and Network Security, Dongguan University of Technology, Dongguan, ChinaSchool of Computer Science and Network Security, Dongguan University of Technology, Dongguan, ChinaThrough modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm. However, BSO is pointed out that it possesses a degenerated L-curve phenomenon, i.e., it often gets near optimum quickly but needs much more cost to improve the accuracy. To overcome this question in this paper, an excellent direct search-based local solver, the Nelder-Mead Simplex method is adopted in BSO. Through combining BSO's exploration ability and NMS's exploitation ability together, a simplex search-based BSO (Simplex-BSO) is developed via a better balance between global exploration and local exploitation. Simplex-BSO is shown to be able to eliminate the degenerated L-curve phenomenon on unimodal functions, and alleviate significantly this phenomenon on multimodal functions. Large number of experimental results shows that Simplex-BSO is a promising algorithm for global optimization problems.https://ieeexplore.ieee.org/document/8546742/Brain storm optimizationNelder-Mead Simplex methodglobal explorationlocal exploitationvisualizing confidence intervals |
| spellingShingle | Wei Chen Yingying Cao Shi Cheng Yifei Sun Qunfeng Liu Yun Li Simplex Search-Based Brain Storm Optimization IEEE Access Brain storm optimization Nelder-Mead Simplex method global exploration local exploitation visualizing confidence intervals |
| title | Simplex Search-Based Brain Storm Optimization |
| title_full | Simplex Search-Based Brain Storm Optimization |
| title_fullStr | Simplex Search-Based Brain Storm Optimization |
| title_full_unstemmed | Simplex Search-Based Brain Storm Optimization |
| title_short | Simplex Search-Based Brain Storm Optimization |
| title_sort | simplex search based brain storm optimization |
| topic | Brain storm optimization Nelder-Mead Simplex method global exploration local exploitation visualizing confidence intervals |
| url | https://ieeexplore.ieee.org/document/8546742/ |
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