Multi-objective design space exploration under uncertainty

In this work, we propose a new technique for efficiently exploring a multiobjective design space to find non-dominated solutions in the presence of uncertainty. Our approach uses a two-stage optimization technique. In the first-stage, the design problem is represented by a multi-objective optimization problem considering the performances associated with design parameters. In the second stage, the design problem is expressed as a single objective optimization problem taking into account the operating performances of the process under uncertainty. We propose a new hybrid algorithm to efficiently explore a multi-objective design space. Our algorithm combines the advantage of both gradient-based and non-gradient-based optimization algorithm. A genetic algorithm (GA) is used to handle the multi-objective optimization problem in the first stage. In comparison, the sequential quadratic programming (SQP) method with problem decomposition is applied to solve the sub-problems in the second stage. Our proposed technique can improve the speed of computation and reduce computational time. The proposed methodology is implemented by integrating in-house software with commercial software. We illustrate the applicability of the proposed methodology in a case study.