Reliability-Based Design Optimization Using Evolutionary Algorithm

In our day-to-day life, a system never works on the conditions specified while it was being designed. Hence, it becomes necessary to include the uncertainties present in these systems while obtaining the optimized conditions. These uncertainties present in a reliability model can be handled well if the probabilistic constraints of such models are satisfied. This paper is directed to handle these probabilistic constraints of a reliability-based design optimization model (RBDO). For this purpose, one of the most efficient and precise optimization tools, namely genetic algorithm (GA), has been used. The basic principle of GAs involves the combination of the fittest string structures which are generated through numerous random iterations. The main objective of this paper is to incorporate the efficient function of a multi-objective evolutionary algorithm (MOEA) in a code formulated in 'C' language which is designed in a manner such that it is capable to handle the probabilistic constraints of a RBDO model.