A note on a reliability redundancy allocation problem using a tuned parameter genetic algorithm

Abstract

This paper presents an improved continuous genetic algorithm (CGA) to optimize the reliability redundancy allocation problem (RRAP) which determines the best redundancy strategies, the number of components, and levels of each subsystem to maximize the system reliability. In this system, both active and cold-standby redundancies can be chosen for individual subsystems. The RRAP belongs to NP-hard problems in the computational complexity theory that is the main reason for employing CGA to solve it. In addition, the response surface methodology (RSM) is used to increase the performance of CGA considering the design of experiments. This algorithm employs a new chromosome so that frees offspring to repair during the evolution process. Considering several numerical examples, the proposed algorithm presents better solutions than the previous studies based on the system reliability. Finally, the conclusion and future research are considered.