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An Improved multi-objective genetic algorithm based on orthogonal design and adaptive clustering pruning strategy
[article]
2019
arXiv
pre-print
Two important characteristics of multi-objective evolutionary algorithms are distribution and convergency. As a classic multi-objective genetic algorithm, NSGA-II is widely used in multi-objective optimization fields. However, in NSGA-II, the random population initialization and the strategy of population maintenance based on distance cannot maintain the distribution or convergency of the population well. To dispose these two deficiencies, this paper proposes an improved algorithm, OTNSGA-II
arXiv:1901.00577v1
fatcat:n4kbjsfujrdpllxhb6csv6al74