PROJECT TITLE :

Granular Risk-Based Design Optimization

ABSTRACT:

Optimization considering uncertainty is an increasingly important and continuously developing uncertainty mitigation technique for trendy design. Compared with its well-established branches, i.e., reliability-based style optimization (RBDO) and robust design optimization, risk-based mostly design optimization (RDO) is just considered an extension of RBDO by incorporating future value; hence, it's not received a lot of deep theoretical study. Based on the generalized theory of uncertainty, we introduce totally different levels of probability granulation into RDO and propose a granular risk-based mostly style optimization (GRDO) methodology. The risks are modeled as granular possibilities, their mean values, and standard deviations. 2 multiobjective optimization (MO) formulations of GRDO are proposed and solved by multiobjective evolutionary algorithm based on decomposition aided with resolution filtering criterion. Based on the results of a structural design example, the aptitude of GRDO on uncertainty management is validated by comparing the performances of different MO formulations, while uncertainty mitigation using GRDO is achieved by controlling the risks associated with fixed level of uncertainty. This way, GRDO is approved as a general style frame rather than simply an uncertainty mitigation technique like standard RDO.