Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Abstract

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.

Prediction Model of Grain Mold Probability Based on Naive Bayes Algorithm

“…Research on GM detection and prediction has yielded good results. In terms of GM detection, some scholars have applied electronic nose technology self-developed electronic nose to conduct experiments to track the process of GM in storage and monitor GM, and found that it can provide a more accurate response to the occurrence of GM in storage and has high sensitivity in monitoring, and some scholars have combined chemometric methods to process and analyze moldy rice images and constructed a model that can accurately distinguish between rice mold Some scholars have combined chemometric methods to process and analyze moldy rice images and constructed models that can accurately distinguish the types of rice mold [3][4]. In terms of GM probability prediction, many large grain stations judge and control the mold condition by real-time monitoring of grain temperature and humidity as well as example sampling, which cannot accurately reflect the mold activity in a timely manner due to the lag in temperature changes, making the prediction results differ significantly from the true values and leading to serious grain damage [5].…”