Improving Estimation of Distribution Algorithm on Multimodal Problems by Detecting Promising Areas

Peng Yang, Ke Tang, Xiaofen Lu

Abstract: In this paper, a novel multiple sub-models maintenance technique, named maintaining and processing sub-models (MAPS), is proposed. MAPS aims to enhance the ability of estimation of distribution algorithms (EDAs) on multimodal problems. The advantages of MAPS over the existing multiple sub-models based EDAs stem from the explicit detection of the promising areas, which can save many function evaluations for exploration and thus accelerate the optimization speed. MAPS can be combined with any EDA that adopts a single Gaussian model. The performance of MAPS has been assessed through empirical studies where MAPS is integrated with three different types of EDAs. The experimental results show that MAPS can lead to much faster convergence speed and obtain more stable solutions than the compared algorithms on 12 benchmark problems.