Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

许多现实世界的优化问题涉及两个不同的变量子集：决策变量，以及那些不存在于成本函数中但限制解决方案的变量，因此在优化过程中必须加以考虑。因此，必须考虑两个变量子集之间和内部的依赖关系。在本文中，实现了分布估计算法（EDA）来解决这类复杂的优化问题。高斯贝叶斯网络用于在每次迭代中建立搜索空间的抽象模型，以识别变量之间的模式。由于算法是从数据初始化的，我们引入了一个新的超参数来控制初始数据在 EDA 执行期间做出的决策中的影响。