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An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization
Joint Authors
Cao, Jie
Yu, Xiaobing
Shan, Haiyan
Zhu, Li
Guo, Jun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-09
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Yu, Xiaobing& Cao, Jie& Shan, Haiyan& Zhu, Li& Guo, Jun. 2014. An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-1048770
Modern Language Association (MLA)
Yu, Xiaobing…[et al.]. An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization. The Scientific World Journal No. 2014 (2014), pp.1-16.
https://search.emarefa.net/detail/BIM-1048770
American Medical Association (AMA)
Yu, Xiaobing& Cao, Jie& Shan, Haiyan& Zhu, Li& Guo, Jun. An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-1048770
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048770