Simulated Annealing Applications
Simulated Annealing Applications
复制标题
模拟退火应用
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
K. Nara
中科院分区：
文献类型：
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作者：
K. Nara
模拟退火法是一种通过模拟金属冷却过程的随机热动力学来求解优化问题的方法。SA通过模拟液态金属从高温状态(内热能最大)缓慢冷却到令人满意的结晶(内热能最小)这一物理事实，得到了最优解。换句话说，泛函最小化对应于熔炉中金属内部热能的最小化。利用这一事实解决优化问题的想法是由Kirkpatric提出的[17]。他证明了该方法成功地适用于所谓的组合优化问题。在参考文献[17]中，他介绍了SA的几个应用(计算机的物理设计、布线和旅行推销员)。在电力系统应用中，许多组合优化问题都是用模拟退火算法来解决的。电力系统领域中存在多种组合优化问题：发电机检修计划、机组组合、无功规划、网络规划、配电网重构等，众所周知，这些问题还没有找到一个既定的求解算法。已有的大多数有效的求解算法只能找到近似的最优解，而不能找到实际问题的精确最优解。众所周知，如果我们仔细而缓慢地降低温度，SA就能找到这些组合优化问题的近最优解。此外，模拟退火法非常简单，如后面几节所示。因此，使用模拟退火算法得到的解经常被用作评估新发展的解算法的性能的参考解，尽管它需要巨大的计算负担。
Simulated Annealing (SA) is a method to solve an optimization problem by simulating a stochastic thermal dynamics of a metal cooling process. SA obtains an optimal solution by simulating a physical fact that liquid metal transmutes to be crystal (which has the smallest internal thermal energy) if it is cooled satisfactory slowly from a high temperature state (with large internal thermal energy). In other words, functional minimization corresponds to minimization of internal thermal energy of metal in a melting pot. The idea to solve the optimization problem by using this fact is proposed by Kirkpatric [17]. He demonstrated that the method is successfully applicable to so-called combinatorial optimization problem. In the reference [17], he has introduced several applications (physical design of computers, wiring and traveling salesmen) of SA. In power system applications, many combinatorial optimization problems have been solved by using SA. There are many kinds of combinatorial optimization problems in power systems area: generator maintenance scheduling, unit commitment, VAR planning, network planning, distribution systems reconfiguration etc. It is well known that we have not yet found an established solution algorithm for these problems. Most efficient solution algorithms proposed for the problems can only find approximated optimal solutions, and we cannot find the exact optimal solution for the practical problem. It is well known that SA can find near optimal solutions for these combinatorial optimization problems if we decrease temperature carefully and slowly enough. Moreover, the simulated annealing algorithm is so simple as shown in later sections. Therefore, solutions obtained by using SA are often used as a reference solution to evaluate the performance of newly developed solution algorithms even though it takes huge computational burden.