Enhancement of Multiobjective Hierarchical Bayesian Optimization Algorithm using Sporadic Model Building

Abstract

This paper describes and analyzes the efficiency enhancement of Multiobjective hierarchical Bayesian Optimization Algorithm (mohBOA) by using Sporadic Model Building (SMB). Firstly, Multiobjective hierarchical Bayesian Optimization Algorithm is shortly described. Secondly, sporadic model building is presented. Using sporadic model building, the structure of a probabilistic model is updated once every few iterations, whereas in the remaining iterations only model parameters (conditional and marginal probabilities) are updated. Since the time of learning the structure of a model is much longer than the time of updating model parameters, sporadic model building decreases the total time complexity of model building. The results of experiments show that the theoretical predictions about using sporadic model building to the enhancement of mohBOA are true. Finally, short discussion about the results of experiments is added.