Abstract
NSGA-II has shown good performance in solving multi-objective optimization problems, However, the tournament selection strategy in NSGA-II always generates many duplicate individuals. This phenomenon not only affects the crossover, mutation and updating operations and finally deteriorates the performance significantly. To overcome this problem, this paper introduces a new strategy, namely selection strategy without replacement, which can produces different individuals to increase the diversity. Simulation results show the proposed tournament selection without replacement achieve better performance.
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Acknowledgement
The paper is supported by the Natural Science Foundation of Shanxi Province under Grant No. 201601D011045, and Graduate Educational Innovation Project of Shanxi Province under Grant No. 2017SY075.
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Wang, Y., Zhu, Z., Zhang, M., Cui, Z., Cai, X. (2018). A New Selection Without Replacement for Non-dominated Sorting Genetic Algorithm II. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_86
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