In this paper, we propose a variant of the reduced Jacobian method (RJM) introduced by El Maghri and Elboulqe (J Optim Theory Appl 179:917–943, 2018) for multicriteria optimization under linear constraints. Motivation is that, contrarily to RJM which has only global convergence to Pareto KKT-stationary points in the classical sense of accumulation points, this new variant possesses the full convergence property in the sense that the entire sequence converges whenever the objectives are quasiconvex. Simulations are reported showing the performance of this variant compared to RJM and the nondominated sorting genetic algorithm (NSGA-II).

在本文中，我们提出了 El Maghri 和 Elboulqe (J Optim Theory Appl 179:917–943, 2018) 引入的简化雅可比方法 (RJM) 的变体，用于线性约束下的多标准优化。动机在于，与经典累积点意义上的 Pareto KKT 平稳点仅具有全局收敛性的 RJM 相反，这种新变体具有完全收敛性，即只要目标是拟凸的，整个序列就收敛。据报道，模拟显示了该变体与 RJM 和非支配排序遗传算法 (NSGA-II) 相比的性能。