Hierarchical Swarm Model: A New Approach to Optimization
This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of H...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2010/379649 |
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| _version_ | 1849414484409450496 |
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| author | Hanning Chen Yunlong Zhu Kunyuan Hu Xiaoxian He |
| author_facet | Hanning Chen Yunlong Zhu Kunyuan Hu Xiaoxian He |
| author_sort | Hanning Chen |
| collection | DOAJ |
| description | This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. |
| format | Article |
| id | doaj-art-72e8678a34ce4e50b5372dc6c5e554da |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2010-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-72e8678a34ce4e50b5372dc6c5e554da2025-08-20T03:33:50ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2010-01-01201010.1155/2010/379649379649Hierarchical Swarm Model: A New Approach to OptimizationHanning Chen0Yunlong Zhu1Kunyuan Hu2Xiaoxian He3Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, Shenyang 110016, ChinaKey Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Faculty Office III, Nanta Street 114#, Dongling District, Shenyang 110016, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410083, ChinaThis paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.http://dx.doi.org/10.1155/2010/379649 |
| spellingShingle | Hanning Chen Yunlong Zhu Kunyuan Hu Xiaoxian He Hierarchical Swarm Model: A New Approach to Optimization Discrete Dynamics in Nature and Society |
| title | Hierarchical Swarm Model: A New Approach to Optimization |
| title_full | Hierarchical Swarm Model: A New Approach to Optimization |
| title_fullStr | Hierarchical Swarm Model: A New Approach to Optimization |
| title_full_unstemmed | Hierarchical Swarm Model: A New Approach to Optimization |
| title_short | Hierarchical Swarm Model: A New Approach to Optimization |
| title_sort | hierarchical swarm model a new approach to optimization |
| url | http://dx.doi.org/10.1155/2010/379649 |
| work_keys_str_mv | AT hanningchen hierarchicalswarmmodelanewapproachtooptimization AT yunlongzhu hierarchicalswarmmodelanewapproachtooptimization AT kunyuanhu hierarchicalswarmmodelanewapproachtooptimization AT xiaoxianhe hierarchicalswarmmodelanewapproachtooptimization |