模拟退火算法来源于固体退火原理，将固体加温至充分高，再让其徐徐冷却，加温时，固体内部粒子随温升变为无序状，内能增大，而徐徐冷却时粒子渐趋有序，在每个温度都达到平衡态，最后在常温时达到基态，内能减为最小。根据Metropolis准则，粒子在温度T时趋于平衡的概率为e-ΔE/(kT)，其中E为温度T时的内能，ΔE为其改变量，k为Boltzmann常数。用固体退火模拟组合优化问题，将内能E模拟为目标函数值f，温度T演化成控制参数t，即得到解组合优化问题的模拟退火算法：由初始解i和控制参数初值t开始-Simulated annealing algorithm comes from solid annealing principle, will warm to fully solid high, then let it slowly cooling, heating, solid internal particles with a temperature rise of disorder, can increase, and gradually cooled gradually orderly particles, and in every temperature at the balance state, and the last in the normal temperature at the ground state, internal energy is reduced to the minimum standards according to the Metropolis, particle in temperature T tend to balance when the probability of e-Δ e/(kT), which for temperature T e the internal energy, Δ e for its change the volume, k as Boltzmann constant use solid annealing simulation combinatorial optimization problem, the internal energy e simulation for target function value f, temperature T evolution into control parameters T, namely get solution combinatorial optimization problem of simulated annealing algorithm: the initial solution I and control parameter optimization.finally T start