Volume 11 Number 8 (Aug. 2016)
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JSW 2016 Vol.11(8): 733-744 ISSN: 1796-217X
doi: 10.17706/jsw.11.8.733-744

A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis

Qiong Yuan1,2, Guangming Dai1,2*
1School of Computer Science, China University of Geosciences, Wuhan 430074, China.
2Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China.

Abstract—According to the traditional GA and EDA weakness, on the basis of MMEA, the orthogonal design initialization, convergence criterion and K-means clustering analysis method were introduced in this paper and it proposed a new model multi-objective evolutionary algorithm OMEA. The practice results showed that the OMEA had been greatly improved on both convergence and diversity of the solutions, reaching a good balance on diversity and convergence. Its comprehensive performance was better than the SPEA2, NSGA-II and other traditional multi-objective evolutionary algorithm.

Index Terms—Multi-objective evolutionary algorithms, orthogonal design initialization, convergence criterion, principal curve model, K-means clustering.

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Cite: Qiong Yuan, Guangming Dai, "A Multi-objective Evolutionary Algorithm of Principal Curve Model Based on Clustering Analysis," Journal of Software vol. 11, no. 8, pp. 733-744, 2016.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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