Adaptive parallel simulated annealing genetic algorithms based on cloud models

作者：Dong Lili^*; Gong Guanghong; Li Ni; Sun Yong

来源：Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(9): 1132-1136.

• Adaptive mechanism
• Adaptive regulation
• Analysis and simulation
• Cloud model
• Cloud models
• Convergence rates
• Convergence speed
• Cross-over probability
• Improved genetic algorithms
• Local optima
• Multi core
• Multi-species
• Mutation probability
• Optimal results
• Parallel
• Parallel executions
• Parallel operations
• Parallel simulated annealing
• Parallel technology
• Premature convergence
• Threading building blocks

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.

• 出版日期2011
• 单位北京大学