期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2021
卷号：48
期号：4
语种：English
出版社：IAENG - International Association of Engineers
摘要：The simulated annealing algorithm is a stochastic optimization method for solving optimization problems associated with a large search space. The efficiency of the algorithm depends on the adaptation of the cooling model. Despite this, the main disadvantage of this algorithm is that it does not take into account the state of the system while searching. Thus, it is difficult to predict the system convergence with the simulated annealing algorithm. However, neural networks in particular continuous Hopfield networks have proven their ability in the field of machine learning to make a decision. In this paper, we introduce continuous Hopfield networks to improve the convergence of the simulated annealing algorithm. The experimental results show that the hybrid approach produces a large number of stable sets.
