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

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 bas...

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Bibliographic Details
Published inIEEE transactions on cybernetics Vol. 45; no. 8; pp. 1438 - 1449
Main Authors Yang, Peng, Tang, Ke, Lu, Xiaofen
Format Journal Article
LanguageEnglish
Published United States IEEE 01.08.2015
Subjects
Clustering algorithms
Covariance matrices
Estimation
Estimation of distribution algorithms (EDAs)
Histograms
Joints
multimodal problems
promising areas
Sociology
multimodal problems
promising areas
Estimation of distribution algorithms (EDAs)
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Summary: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.
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ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2014.2352411