A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy

Distributed Parallel Genetic Algorithm is the most widely a parallel genetic algorithm. It has natural parallelism and has high performance in solving complex, large-scale, non-linear, non-differentiable optimization problems. This paper analyzes the traditional limitations of distributed parallel g...

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Published in:2012 8th International Conference on Natural Computation pp. 592 - 595
Main Authors: Wei Li, Ying Huang
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2012
Subjects:
Adaptive Migration Strategy
Algorithm design and analysis
Computers
Convergence
Distributed Parallel Algorithm
Educational institutions
Function Optimization
Genetic Algorithm
Genetic algorithms
Optimization
Synchronization
ISBN:9781457721304, 1457721309
ISSN:2157-9555
Online Access:Get full text
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Abstract Distributed Parallel Genetic Algorithm is the most widely a parallel genetic algorithm. It has natural parallelism and has high performance in solving complex, large-scale, non-linear, non-differentiable optimization problems. This paper analyzes the traditional limitations of distributed parallel genetic algorithms, for its migration fixed blindness and other disadvantages. A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy (AMDPGA) was proposed in this paper, which was suitable for running on the current parallel computers. This Implementation combines the Distributed Parallel Genetic Algorithm and current computer architecture, which makes the Distributed Parallel Genetic Algorithm execute on the mainstream computer concurrently and improve the convergent speed. The experiments showed that this algorithm has not only faster convergent speed but also has more accurate precision and overcome more faults as well as higher parallel efficiency.
AbstractList Distributed Parallel Genetic Algorithm is the most widely a parallel genetic algorithm. It has natural parallelism and has high performance in solving complex, large-scale, non-linear, non-differentiable optimization problems. This paper analyzes the traditional limitations of distributed parallel genetic algorithms, for its migration fixed blindness and other disadvantages. A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy (AMDPGA) was proposed in this paper, which was suitable for running on the current parallel computers. This Implementation combines the Distributed Parallel Genetic Algorithm and current computer architecture, which makes the Distributed Parallel Genetic Algorithm execute on the mainstream computer concurrently and improve the convergent speed. The experiments showed that this algorithm has not only faster convergent speed but also has more accurate precision and overcome more faults as well as higher parallel efficiency.
Author Ying Huang
Wei Li
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  surname: Wei Li
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  surname: Ying Huang
  fullname: Ying Huang
  email: nhwshy@gmail.com
  organization: Sch. of Math. & Comput. Sci., Gannan Normal Univ., Ganzhou, China
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Snippet Distributed Parallel Genetic Algorithm is the most widely a parallel genetic algorithm. It has natural parallelism and has high performance in solving complex,...
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SubjectTerms Adaptive Migration Strategy
Algorithm design and analysis
Computers
Convergence
Distributed Parallel Algorithm
Educational institutions
Function Optimization
Genetic Algorithm
Genetic algorithms
Optimization
Synchronization
Title A Distributed Parallel Genetic Algorithm oriented adaptive migration strategy
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