Research on the Initial Value of the Simulated Annealing
Research on the Initial Value of the Simulated Annealing
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DOI：
10.4028/www.scientific.net/amr.774-776.1770
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发表时间：
2013-09
期刊：
影响因子：
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通讯作者：
X. W. Liang;Wei Gong;W. Fu;Jing Qi
中科院分区：
文献类型：
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作者：
X. W. Liang;Wei Gong;W. Fu;Jing Qi
模拟退火算法是十大经典优化算法之一，已成功应用于各个领域。模拟退火算法是一种能够找到全局最优解的优化算法，与神经网络算法相比，它易于实现，被采用的概率更高，但与其他优化算法一样，它也有自己的缺点，其结果在很大程度上取决于初始值，传统的模拟退火算法的初始值从一个随机数开始，其收敛速度往往很慢，效果很差。本文提出了一种基于遗传算法和爬坡法的模拟退火算法，由于爬坡算法容易陷入局部最优，而模拟退火刚好可以解决这一问题，不仅避免了局部最优，而且收敛速度和结果都很好。
Simulated Annealing Algorithm is one of the top ten classical optimization algorithm, and it has been successfully applied to various fields. Simulated annealing is a optimization algorithm which can find the global optimal solution, compares to neural network algorithm, it is so easily to implement that has higher probability to be adopted, but it has own shortcomings like other optimization algorithms, its result largely depends on initial value, The initial value of the traditional simulated annealing algorithm began with a random number, its convergence speed is often slow very much and the effect is bad. In this paper, a new simulated annealing algorithm that based on genetic algorithm and hill-climbing method was brought up, because of hill-climbing algorithm was easy to fall into local optimum, and simulated annealing can just solve the problem, it not only escaped from local optimum, but also got good convergence speed and results.