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- W2342630703 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." @default.
- W2342630703 created "2016-06-24" @default.
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- W2342630703 date "2016-10-01" @default.
- W2342630703 modified "2023-10-14" @default.
- W2342630703 title "A Self-Organizing Multiobjective Evolutionary Algorithm" @default.
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- W2342630703 doi "https://doi.org/10.1109/tevc.2016.2521868" @default.
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