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A Self-Organizing Multiobjective Evolutionary Algorithm
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DOI：10.1109/TEVC.2016.2521868.png)
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摘要
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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.
Keyword:
Clustering algorithm
evolutionary algorithms
multiobjective optimization
self-organizing map (SOM)
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Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
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