Optimization of Multiobjective System Reliability Design Using FLC controlled GA

A practical optimal reliability design of a system required high system reliability could be formulated as an appropriate mathematical programming model, however, because in the real world, we should concern some kinds of decision criteria. Particularly, system reliability and construction cost are basically conflict each other, so that when taking both of them into consideration, the system reliability design model can be formulated as a bi-objective mathematical programming model. In this research, we consider a bi-criteria redundant system reliability design problem which is optimized by selecting and assigning system components among different valuable candidates for constructing a series-parallel redundant system. Such a problem is formulated as a bi-criteria nonlinear integer programming (bi-nIP) model. In the past decade, several researchers have developed many heuristic algorithms including genetic algorithms (GAs) for solving multi-criteria system reliability optimization problems and obtained acceptable and satisfactory results. Unfortunately, the Pareto solutions obtained by solving a multi-objective optimization problem using a GA cannot be guaranteed its quality, and the number of the Pareto solutions obtained is sometimes not so many. In order to overcome such problems, we propose a hybrid genetic algorithm combined with a Fuzzy Logic Controller (FLC) and a local search technique to obtain the Pareto solutions as many and good as possible. The efficiency of the proposed method is demonstrated through comparative numerical experiments.