模拟退火算法的并行化策略研究

The Research on Parallel Algorithm of Simulation Annealing

模拟退火算法是一种能应用到求最小值问题或连续更新的学习过程（随机或决定性的）。在此过程中，每一步更新过程的长度都与相应的参数成正比，这些参数扮演着温度的角色。标准模拟退火算法仅进行串行优化，其效率很难提高。因此，考虑引入多种群群体优化机制构造并行算法，并对接受准则进行讨论。

Simulation Annealing is a technique which can be applied to any minimization or learning process based on successive update steps （either random or deterministic） where the update step length is proportional to an arbitrarily set parameter which can play the role of a temperature. Simulation annealing only use serial optimize method, it＇s difficult to improve efficiency. So, we try to use multi-colony optimize mechanism to form parallel algorithm, and discussed accept rule.