A Self-Organizing Multiobjective Evolutionary Algorithm

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...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 20; H. 5; S. 792 - 806
Hauptverfasser: Zhang, Hu, Zhou, Aimin, Song, Shenmin, Zhang, Qingfu, Gao, Xiao-Zhi, Zhang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2016
Schlagworte:
Algorithm design and analysis
Clustering algorithm
Electronic mail
evolutionary algorithms
Evolutionary computation
multiobjective optimization
self-organizing map (SOM)
Shape
Sociology
Statistics
Training
ISSN:1089-778X, 1941-0026
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Zusammenfassung: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.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2521868