A Self-Organizing Multiobjective Evolutionary Algorithm

Research output: Research - peer-review21_Publication in refereed journal

9 Scopus Citations
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  • Hu Zhang
  • Aimin Zhou
  • Shenmin Song
  • Xiao-Zhi Gao
  • Jun Zhang

Related Research Unit(s)


Original languageEnglish
Article number7393537
Pages (from-to)792-806
Journal / PublicationIEEE Transactions on Evolutionary Computation
Issue number5
StatePublished - 1 Oct 2016


Under mild conditions, the Pareto front (Pareto set) of a continuous m-objective optimization problem forms an (m-1)-dimensional piecewise continuous manifold. Based on this property, this paper proposes a self-organizing multiobjective evolutionary algorithm. At each generation, a self-organizing mapping method with (m-1) latent variables is applied to establish the neighborhood relationship among current solutions. A solution is only allowed to mate with its neighboring solutions to generate a new solution. To reduce the computational overhead, the self-organizing training step and the evolution step are conducted in an alternative manner. In other words, the self-organizing training is performed only one single step at each generation. The proposed algorithm has been applied to a number of test instances and compared with some state-of-the-art multiobjective evolutionary methods. The results have demonstrated its advantages over other approaches.

Research Area(s)

  • Clustering algorithm, evolutionary algorithms, multiobjective optimization, self-organizing map (SOM)

Citation Format(s)

A Self-Organizing Multiobjective Evolutionary Algorithm. / Zhang, Hu; Zhou, Aimin; Song, Shenmin; Zhang, Qingfu; Gao, Xiao-Zhi; Zhang, Jun.

In: IEEE Transactions on Evolutionary Computation, Vol. 20, No. 5, 7393537, 01.10.2016, p. 792-806.

Research output: Research - peer-review21_Publication in refereed journal