Application of a multi-objective genetic algorithm to solve reliability optimization problem

Presence of multiple objectives in a problem, in principle, gives rise to a set of optimal solution (largely known as Pareto-optimal solution), instead of single optimal solution. This type of problem is known as multi-objective optimization problem (MOP). In general a MOP has been solved using weighted sums or decision-making schemes. An alternative way is to look for the Pareto-optimal front. Many evolutionary algorithms (EAs) like genetic algorithm (GA) have been suggested to solve a MOP, hence termed as multi-objective evolutionary algorithms (MOEAs). Nondominated sorting genetic algorithm (NSGA-II) is one such MOEA which demonstrates the ability to indentify a Pareto-optimal front efficiently. Thus, it provides the decision maker (DM) a complete picture of the optimal solution space. This paper presents an application of NSGA-II in order to solve a multi-objective series system reliability optimization problem. Here, conflicting objectives such as maximization of system reliability and minimization of the system cost have been considered Supremacy of the approach over the existing approach have been depicted and discussed through the results obtained.