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A Self-Organizing Multiobjective Evolutionary Algorithm

Zhang, Hu and Zhou, Aimin and Song, Shenmin and Zhang, Qingfu and Gao, Xiao-Zhi and Zhang, Jun (2016) 'A Self-Organizing Multiobjective Evolutionary Algorithm.' IEEE Transactions on Evolutionary Computation, 20 (5). pp. 792-806. ISSN 1089-778X

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Clustering algorithm; evolutionary algorithms; multiobjective optimization; self-organizing map (SOM)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 14 Dec 2016 09:44
Last Modified: 18 Aug 2022 13:28
URI: http://repository.essex.ac.uk/id/eprint/18564

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