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为了提高多目标遗传算法NSGA_Ⅱ的计算速度,改进了原来算法的分层方式,设计了新的拥挤距计算算子和带有约束机制的外部集,使得改进后的算法在很大程度上提高了解的多样性。使用典型的应用函数进行测试,结果表明:改进的算法在保持NSGA_Ⅱ高效收敛性的基础上,提高了算法的计算效率,改善了解的多样性分布。
In order to improve the computational speed of multi-objective genetic algorithm (NSGA_II), the original algorithm is improved in terms of the stratification method, a new congestion distance calculation operator and an external set with constraint mechanism are designed, which makes the improved algorithm greatly improve the understanding Diversity. The typical application function is used to test the algorithm. The results show that the improved algorithm can improve the computational efficiency and improve the diversity of the solution based on the efficient convergence of NSGA_II.