论文部分内容阅读
针对遗传算法收敛速度慢,容易“早熟”等缺点,提出了一种改进的遗传算法,即基于云模型的自适应并行模拟退火遗传算法(PCASAGA,Adaptive Parallel Simulated An-nealing Genetic Algorithms Based On Cloud Models).PCASAGA使用云模型实现交叉概率和变异概率的自适应调节;结合模拟退火避免遗传算法陷入局部最优;使用多种群优化机制实现算法的并行操作;使用英特尔推出的线程构造模块(TBB,Threading Building Blocks)并行技术,实现算法在多核计算机上的并行执行.理论分析和仿真结果表明:该算法比其他原有的或改进的遗传算法具有更快的收敛速度和更好的寻优结果,并且充分利用了当前计算机的多核资源.
Aiming at the shortcomings of slow convergence speed and easy “precocity” of genetic algorithm, this paper proposes an improved genetic algorithm, namely, Adaptive Parallel Simulated Annealing Genetic Algorithm (PCASAGA) based on cloud model Cloud Models) .PCASAGA uses cloud model to adaptively adjust the crossover probability and mutation probability. Simulated annealing is used to avoid the genetic algorithm getting into local optimum. Parallel algorithm is implemented by using multiple population optimization mechanisms. Using Intel’s thread building module (TBB , Threading Building Blocks) parallel technology to realize parallel execution of algorithms on multi-core computers.Theoretical analysis and simulation results show that the proposed algorithm has faster convergence speed and better search results than other original or improved genetic algorithms , And make full use of the current computer’s multi-core resources.