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Citation: Dong Lili, Gong Guanghong, Li Ni, et al. Adaptive parallel simulated annealing genetic algorithms based on cloud models[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(9): 1132-1136. (in Chinese)

Adaptive parallel simulated annealing genetic algorithms based on cloud models

  • Received Date: 20 Apr 2010
  • Publish Date: 30 Sep 2011
  • Due to the shortcomings of genetic algorithms such as the low convergence rate and premature convergence, an improved genetic algorithms was proposed, called adaptive parallel simulated annealing genetic algorithms based on cloud models (PCASAGA). PCASAGA applied cloud models to the adaptive regulation of the crossover probability and mutation probability. Simulated annealing was combined to prevent genetic algorithms from local optimum. Multi-species optimization mechanism was used to realize algorithm parallel operation. Intel-s threading building blocks (TBB) parallel technology was also used to realize algorithm parallel execution on multi-core computers. Theoretical analysis and simulation results verify that PCASAGA has better convergence speed and optimal results than original or improved genetic algorithms, and it takes full advantage of the current computers multi-core resources.

     

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