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Journal of Software Engineering
  Year: 2016 | Volume: 10 | Issue: 4 | Page No.: 448-456
DOI: 10.3923/jse.2016.448.456
A Self-adaptive Cloud Co-evolution Genetic Algorithm for Parameter Identification of Advanced Manufacturing Mode Diffusion
Xue Chaogai, Sheng Yu and Cao Haiwang

Abstract:
Background: To solve the parameter identification of a multiple advanced-manufacturing-mode competitive diffusion (ACD), this study proposes an improved self-adaptive cloud co-evolution genetic algorithm (ISCCGA) combining a cloud differential evolution model with competitive strategies. Materials and Methods: First, the multiple-ACD model is described and the parameter identification model is formulated. Then, to solve the parameter identification of multiple-ACD model, ISCCGA is proposed in which differential evolution of a cloud model and competitive strategies are introduced into the crossover operator to improve the convergence speed and global search ability. In addition, the co-evolution and mutation probabilities are improved to implement nonlinear adaptive adjustment. And then optimal parameters are obtained. Results: Finally, the influences of parameters on the algorithm are investigated and the validity of ISCCGA is verified. Conclusion: This experimental results show that ISCCGA is more efficient for parameter identification problem in terms of accuracy and convergence than simple genetic, adaptive genetic and co-evolution adaptive genetic algorithms.
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How to cite this article:

Xue Chaogai, Sheng Yu and Cao Haiwang, 2016. A Self-adaptive Cloud Co-evolution Genetic Algorithm for Parameter Identification of Advanced Manufacturing Mode Diffusion. Journal of Software Engineering, 10: 448-456.

DOI: 10.3923/jse.2016.448.456

URL: https://scialert.net/abstract/?doi=jse.2016.448.456

 
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