Rahman Dashti

In designing engineering systems, definitive solutions can hardly be applied to actual scenarios. This issue is mainly originated from production constraints and the environmental conditions of the actual systems under exploitation. Therefore, a small change in the design variables vector may lead to a significant change in the optimal design that minimizes the objective functions. Hence, it is important to develop methods that provide optimal (or even sub-optimal) solutions with less sensitivity to the uncertainty of the design variables. This is the focus of this paper. We present a robust Non-dominated-Sorting Genetic Algorithm II (NSGA-II)-based multi-objective constrained optimization algorithm. To further illustrate the method, the proposed algorithm is used in the robust and constrained optimal design of a sample engineering system. Evaluation of the obtained results shows that multi-objective engineering problems can be solved by the multi-objective robust optimization (MORO) through finding Pareto solutions, so that by changing the problem parameters, the changes of the solutions will be within an acceptable range.