In this paper, we test the convergence speed of the fast elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) on the Gross Domestic Product (GDP) quarterly disaggregation problem. In fact, we perform many simulations by considering various inputs of the NSGA-II parameters with respect to the population size (pop_size), the iteration number (no_rum), the maximum number of generations (gen_max) and the mutation distribution index in the polynomial mutation (etam). It turns out that for suitably large values of the parameters pop_size, no_rum and gen_max, the NSGA-II algorithm converges to a single Pareto-optimal solution located in a fixed cuboid. Due to the elitism in the NSGA-II and our simulation results, we suspect that the mutation distribution index alone doesn’t influence the time of algorithm convergence and the number of Pareto-optimal solutions. Therefore, we reach the most likely conclusion that only an appropriate choice of the parameter triplet (pop_size, no_rum, gen_max) can ensure the convergence of the algorithm toward a fixed Pareto-optimal solution for the Quadratic Multi-objective Programming of GDP quarterly disaggregation. It is worth noted that our work is an extension of previous works in the same field.
展开▼
机译：在本文中，我们测试了在国内生产总值（GDP）季度分解问题上的快速精油非主导分类遗传算法（NSGA-II）的收敛速度。实际上，我们通过考虑相对于群体大小（POP_SIZE），迭代号（NO_RUM），多项式数（GEN_MAX）以及多项式中的突变分布索引的各种输入来执行许多模拟突变（EtaM）。事实证明，对于POP_SIZE，NO_RUM和GEN_MAX的参数的适当值，NSGA-II算法会收敛到位于固定长方体中的单个PAROTO-OPTOLAL解决方案。由于NSGA-II和我们的仿真结果中的精英主义，我们怀疑单独的突变分布指数不会影响算法收敛时间和静态最佳解决方案的数量。 Therefore, we reach the most likely conclusion that only an appropriate choice of the parameter triplet (pop_size, no_rum, gen_max) can ensure the convergence of the algorithm toward a fixed Pareto-optimal solution for the Quadratic Multi-objective Programming of GDP quarterly disaggregation.值得注意的是，我们的工作是在同一领域的先前作品的扩展。
展开▼
