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.

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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.