An interval sequential linear programming for nonlinear robust optimization problems

In this paper, interval sequential linear programming (ISLP) is proposed to solve nonlinear robust optimization (RO). The main idea of the programming is to transform the uncertain optimization into several possibility-sensitivity analyses and deterministic linear optimization problems that are sequentially solved. At each cycle, a possibility-sensitivity analysis method is proposed to obtain the approximate partial derivatives of the uncertain constraints at the current design point, based on which a deterministic linear optimization model is constructed and the design point is updated by solving the linear optimization. Moreover, an iterative mechanism is created to adaptively update the design space and improve the convergence rate. Finally, two numerical examples and two practical engineering problems are applied to verify the accuracy and efficiency of the proposed method.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a method called Interval Sequential Linear Programming (ISLP) to solve nonlinear robust optimization problems. The method transforms the uncertain optimization problem into a series of possibility-sensitivity analyses and deterministic linear optimization problems, and includes an iterative mechanism to improve convergence rate.