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A Globally Convergent SQCQP Method for Multiobjective Optimization Problems
SIAM Journal on Optimization ( IF 2.6 ) Pub Date : 2021-01-12 , DOI: 10.1137/18m1182152
Md Abu Talhamainuddin Ansary , Geetanjali Panda

SIAM Journal on Optimization, Volume 31, Issue 1, Page 91-113, January 2021.
In this article, the concept of the single-objective sequential quadratically constrained quadratic programming method is extended to the multiobjective case and a new line search technique is developed for nonlinear multiobjective optimization problems. The proposed method ensures global convergence as well as spreading of the Pareto front. A descent direction is obtained by solving a quadratically constrained quadratic programming subproblem. A nondifferentiable penalty function is used to restrict the constraint violations. Convergence of the descent sequence is established under the Mangasarian--Fromovitz constraint qualification and some mild assumptions. In addition to this, a new technique is designed for selecting initial points to ensure the spreading of the Pareto front. The method is compared with existing methods using a set of test problems.


中文翻译:

多目标优化问题的全局收敛SQCQP方法

SIAM优化杂志,第31卷,第1期,第91-113页,2021年1月。
在本文中,将单目标顺序二次约束二次规划方法的概念扩展到多目标情况,并为非线性多目标优化问题开发了一种新的线搜索技术。所提出的方法确保了全局收敛以及帕累托前沿的扩展。通过解决二次约束二次规划子问题来获得下降方向。使用不可微分的惩罚函数来限制约束违例。下降序列的收敛是在Mangasarian-Fromovitz约束条件和一些温和假设下建立的。除此之外,还设计了一种用于选择初始点的新技术,以确保帕累托前沿的扩展。
更新日期:2021-03-21
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