Fault-tolerant design involves different scenarios, such as scenarios with no fault in the system, with faults occurring randomly, with different operation conditions, and with different loading conditions. For each scenario, there can be multiple requirements (objectives). To assess the performance of a design (solution), it needs to be evaluated over a number of different scenarios containing various requirements in each scenario. We consider this problem as a multi-scenario, multi-objective (MSMO) problem.Despite its practical importance and prevalence in engineering application, there are not many studies which systematically solve the MSMO problem. In this dissertation, we focus on optimizing and modeling MSMO problems, and propose various approaches to solve different types of MSMO optimization problems, especially multi-objective fault-tolerant problems. We classify MSMO optimization problem into two categories: scenario-dependent and scenario-independent. For the scenario-dependent MSMO problem, we review existing methodologies and suggest two evolutionary-based methods for handling multiple scenarios and objectives: aggregated method and integrated method. The effectiveness of both methods are demonstrated on several case studies including numerical problems and engineering design problems. The engineering problems include cantilever-type welded beam design, truss bridge design, four-bar truss design. The experimental results show that both methods can find a set of widely distributed solutions that are compromised among the respective objective values under all scenarios. We also model fault-tolerant programs using the aggregated method. We synthesize three fault-tolerant distributed programs: Byzantine agreement program, token ring circulation program and consensus program with failure detector $S$. The results show that evolutionary-base MSMO approach, as a generic method, can effectively model fault-tolerant programs. For the scenario-independent MSMO problem, we apply evolutionary multi-objective approach. As a case study, we optimize a probabilistic self-stabilizing program, a special type of fault-tolerant program, and obtain several interesting counter-intuitive observations under different scenarios.
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