Reliability optimization of systems with component improvement cost based on importance measure

The reliability optimization problem arises along with the increasing demands for products' performance in practical engineering. Importance measures are capable of selecting critical components to gain the greatest improvement in system reliability with the constraint of maintenance cost. The characteristics of importance measure for four kinds of typical systems are discussed to illustrate the usage of importance measure in the practical reliability optimization. A reliability optimization model is eventually established and importance measure-based genetic algorithms are developed to solve the reliability optimization problem efficiently. Finally, two numerical experiments are implemented based on the smoke alarm systems. Experiment I is to illustrate the effectiveness of the importance measure-based genetic algorithms compared with standard genetic algorithm. Finally, the relationship between the order of component reliability improvement and the component parameters is analyzed in Experiment II.