| description abstract | Uncertainty is a very critical but inevitable issue in design optimization. Compared to singleobjective optimization problems, the situation becomes more difficult for multiobjective engineering optimization problems under uncertainty. Multiobjective robust optimization (MORO) approaches have been developed to find Pareto robust solutions. While the literature reports on many techniques in MORO, few papers focus on using multiobjective differential evolution (MODE) for robust optimization (RO) and performance improvement of its solutions. In this article, MODE is first modified and developed for RO problems with interval uncertainty, formulating a new MODERO algorithm. To improve the solutions’ quality of MODERO, a new hybrid (MODEsequential quadratic programming (SQP)RO) algorithm is proposed further, where SQP is incorporated into the procedure to enhance the local search. The proposed hybrid approach takes the advantage of MODE for its capability of handling notwell behaved robust constraint functions and SQP for its fast local convergence. Two numerical and one engineering examples, with two or three objective functions, are tested to demonstrate the applicability and performance of the proposed algorithms. The results show that MODERO is effective in solving MORO problems while, on the average, MODESQPRO improves the quality of robust solutions obtained by MODERO with comparable numbers of function evaluations. | |
