【摘要】 本文以广义Boltzmann－Gibbs统计理论为基础，采用依赖于温度的似Cauchy分布产生新的扰动模型，建立一种新的快速模拟退火算法。文中给出了这种条件下的接收概率和降温方式的具体计算公式。新算法可在高温情况下进行大范围的搜索，在低温时仅在当前模型附近进行搜索，而且由于似Cauchy分布有一平坦的“尾巴”，使其易于跳出局部极值，从而加快了这种模拟退火算法的收敛速度。更多还原

【Abstract】 A fast simulated annealing algorithm has been formed by using both the generalized Boltzmann-Gibbs statistics and the turbulent model derived from temperature-dependent pseudo Cauchy distributi0n. The formulae for determining the acceptance probability and temperature lowering way under such conditions are givenrespectively. The new algorithm achieves big scope search at high temperature andminor scope search around the model at low temperature. The smooth"tail"of pseudo Cauchy distribution favours avoiding the restriction of local extremums,so thatthe simulated annealing alogorithm converges very fast.更多还原