论文标题
在Asplund空间中可靠的多目标优化编程的近似解决方案
Approximate solutions for robust multiobjective optimization programming in Asplund spaces
论文作者
论文摘要
在本文中，我们研究了一个非平滑/非convex多目标优化问题，并且在任意asplund空间中存在不确定的限制。我们首先以模糊形式提供必要的最佳条件，以近似弱强大的有效解决方案，然后建立必要的最佳定理，以在限制细分的含义上，通过利用Freechet subdifitiental的模糊最佳条件来利用限制细分的限制量效率。在广义伪凸函数的新概念下，还驱动了足够的（弱）鲁棒效率解决方案的足够条件。最后，我们解决了参考问题的近似Mond-weir型双重鲁棒问题，并在伪凸度的假设下探索弱，强和相反的二元性能。
In this paper, we study a nonsmooth/nonconvex multiobjective optimization problem with uncertain constraints in arbitrary Asplund spaces. We first provide necessary optimality condition in a fuzzy form for approximate weakly robust efficient solutions and then establish necessary optimality theorem for approximate weakly robust quasi-efficient solutions of the problem in the sense of the limiting subdifferential by exploiting a fuzzy optimality condition in terms of the Frechet subdifferential. Sufficient conditions for approximate (weakly) robust quasi-efficient solutions to such a problem are also driven under the new concept of generalized pseudo convex functions. Finally, we address an approximate Mond-Weir-type dual robust problem to the reference problem and explore weak, strong, and converse duality properties under assumptions of pseudo convexity.
