Solving the redundancy allocation problem with multiple strategy choices using a new simplified particle swarm optimization

In most research on redundancy allocation problem (RAP), the redundancy strategy for each subsystem is assumed to be predetermined and fixed. This paper focuses on a specific RAP with multiple strategy choices (RAP-MSC), in which both active redundancy and cold standby redundancy can be selected as an additional decision variable for individual subsystems. To do so, the component type, redundancy strategy and redundancy level for each subsystem should be chosen subject to the system constraints appropriately such that the system reliability is maximized. Meanwhile, imperfect switching for cold standby redundancy is considered and a k-Erlang distribution is introduced to model the time-to-failure component as well. Given the importance and complexity of RAP-MSC, we propose a new efficient simplified version of particle swarm optimization (SPSO) to solve such NP-hard problems. In this method, a new position updating scheme without velocity is presented with stochastic disturbance and a low probability. Moreover, it is compared with several well-known PSO variants and other state-of-the-art approaches in the literature to evaluate its performance. The experiment results demonstrate the superiority of SPSO as an alternative for solving the RAP-MSC.