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- W2068700557 abstract "In order to improve the performance of multi-objective genetic algorithms, an improved multi-objective genetic algorithm based on granular ranking and distant reproduction (IMOGA) is proposed. In this algorithm, the concept granularity is introduced into multi-objective ranking, and a selection method based on granular ranking is used. Meanwhile, the consanguinity feature is fused into individuals, and a crossover method based on distant reproduction is used. Experiments were taken on multi-objective functions with constraints, the validity of IMOGA was proved. Compared with several multi-objective optimization algorithms such as NSGAII, MOQCGA and MOPSO, the solutions of IMOGA are more excellent, and its robustness is better." @default.
- W2068700557 created "2016-06-24" @default.
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- W2068700557 date "2011-09-01" @default.
- W2068700557 modified "2023-09-27" @default.
- W2068700557 title "An Improved Multi-objective Genetic Algorithm Based on Granular Ranking and Distant Reproduction" @default.
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- W2068700557 doi "https://doi.org/10.1109/icm.2011.66" @default.
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