摘要：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.
