A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization
Large-scale optimization is a challenging problem because it involves a large number of decision variables. In this paper, a simple but effective method, called hierarchical sorting swarm optimizer (HSSO), is proposed for large-scale optimization. As a variant of representative particle swarm optimi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8669682/ |
| _version_ | 1818668273275764736 |
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| author | Rushi Lan Li Zhang Zhiling Tang Zhenbing Liu Xiaonan Luo |
| author_facet | Rushi Lan Li Zhang Zhiling Tang Zhenbing Liu Xiaonan Luo |
| author_sort | Rushi Lan |
| collection | DOAJ |
| description | Large-scale optimization is a challenging problem because it involves a large number of decision variables. In this paper, a simple but effective method, called hierarchical sorting swarm optimizer (HSSO), is proposed for large-scale optimization. As a variant of representative particle swarm optimizer (PSO), HSSO first sorts the initial particles according to their fitness values, and then partitions the sorted particles into two groups, namely, the good group corresponding to better fitness values, and the bad group with worse fitness values. The bad group is then updated by learning from the good one. After that, we take the good group as a new swarm and conduct the sorting and learning procedures. The aforementioned operations are repeated several times until only one particle left to form a hierarchical structure. In the experiments, HSSO is applied to optimize 39 benchmark test functions. The comparative results with several existing algorithms demonstrate that, despite its simplicity, HSSO shows improved performance in terms of both exploration and exploitation. |
| first_indexed | 2024-12-17T06:33:42Z |
| format | Article |
| id | doaj.art-5624aadcdcdf40f8aa3e77a769fd0c35 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:33:42Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5624aadcdcdf40f8aa3e77a769fd0c352022-12-21T22:00:04ZengIEEEIEEE Access2169-35362019-01-017406254063510.1109/ACCESS.2019.29060828669682A Hierarchical Sorting Swarm Optimizer for Large-Scale OptimizationRushi Lan0https://orcid.org/0000-0002-9488-8236Li Zhang1Zhiling Tang2Zhenbing Liu3Xiaonan Luo4Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, ChinaGuangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, ChinaGuangxi Key Laboratory of Wireless Broadband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin, ChinaGuangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, ChinaNational and Local Joint Engineering Research Center of Satellite Navigation and Location Service, Guilin University of Electronic Technology, Guilin, ChinaLarge-scale optimization is a challenging problem because it involves a large number of decision variables. In this paper, a simple but effective method, called hierarchical sorting swarm optimizer (HSSO), is proposed for large-scale optimization. As a variant of representative particle swarm optimizer (PSO), HSSO first sorts the initial particles according to their fitness values, and then partitions the sorted particles into two groups, namely, the good group corresponding to better fitness values, and the bad group with worse fitness values. The bad group is then updated by learning from the good one. After that, we take the good group as a new swarm and conduct the sorting and learning procedures. The aforementioned operations are repeated several times until only one particle left to form a hierarchical structure. In the experiments, HSSO is applied to optimize 39 benchmark test functions. The comparative results with several existing algorithms demonstrate that, despite its simplicity, HSSO shows improved performance in terms of both exploration and exploitation.https://ieeexplore.ieee.org/document/8669682/Large-scale optimizationhierarchical sorting swarm optimizerhierarchical learningparticle swarm optimization |
| spellingShingle | Rushi Lan Li Zhang Zhiling Tang Zhenbing Liu Xiaonan Luo A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization IEEE Access Large-scale optimization hierarchical sorting swarm optimizer hierarchical learning particle swarm optimization |
| title | A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization |
| title_full | A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization |
| title_fullStr | A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization |
| title_full_unstemmed | A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization |
| title_short | A Hierarchical Sorting Swarm Optimizer for Large-Scale Optimization |
| title_sort | hierarchical sorting swarm optimizer for large scale optimization |
| topic | Large-scale optimization hierarchical sorting swarm optimizer hierarchical learning particle swarm optimization |
| url | https://ieeexplore.ieee.org/document/8669682/ |
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