An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization
Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/215472 |
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| author | Xiaobing Yu Jie Cao Haiyan Shan Li Zhu Jun Guo |
| author_facet | Xiaobing Yu Jie Cao Haiyan Shan Li Zhu Jun Guo |
| author_sort | Xiaobing Yu |
| collection | DOAJ |
| description | Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. |
| format | Article |
| id | doaj-art-d9c34bf1883948039ac431e2ba8e8fae |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d9c34bf1883948039ac431e2ba8e8fae2025-02-03T01:23:40ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/215472215472An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global OptimizationXiaobing Yu0Jie Cao1Haiyan Shan2Li Zhu3Jun Guo4China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaChina Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaChina Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaChina Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, ChinaParticle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.http://dx.doi.org/10.1155/2014/215472 |
| spellingShingle | Xiaobing Yu Jie Cao Haiyan Shan Li Zhu Jun Guo An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization The Scientific World Journal |
| title | An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization |
| title_full | An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization |
| title_fullStr | An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization |
| title_full_unstemmed | An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization |
| title_short | An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization |
| title_sort | adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization |
| url | http://dx.doi.org/10.1155/2014/215472 |
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