MULTIOBJECTIVE SYSTEM RELIABILITY OPTIMIZATION USING GENETIC ALGORITHM

Abstract

As many real-world engineering design problems are multi-objective nature and the components are characterized by having different performance levels, cost, weight, and reliability. The multi-objective formulation considered for both of them is the maximization of system availability, and the minimization of both system cost, and weight. 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. Many evolutionary algorithms (EAs) like genetic algorithm (GA) have been suggested to solve a MOP, hence termed as multi-objective evolutionary algorithms (MOEAs). Non-dominated sorting genetic algorithm (NSGA-II) is one such MOEA which demonstrates the ability to identify a Pareto-optimal front efficiently. Thus, it provides the decision maker (DM) a complete picture of the optimal solution space. In this work an application of NSGA-II in order to solve a multi-objective series system reliability optimization problem is described. Here, conflicting objectives such as maximization of system reliability and minimization of the system cost and weight have been considered. Supremacy of the approach over the existing approach have been depicted and discussed through the results obtained. To demonstrate how these methods work, the well-known redundancy allocation problem was solved as a multiple objective problem by using NSGA-II initially to find the Pareto optimal solutions.