Developing Model of Fuzzy Constraints Based on Redundancy Allocation Problem by an Improved Swarm Algorithm

This study aims to develop a novel credibility-based fuzzy model for the constraints of a series-parallel redundancy allocation problem (RAP) with a mix of components under an uncertainty environment and investigates the reliability enhancement of wireless sensor networks (WSN) through a RAP for objective system reliability by subjecting it to two nonlinear constraints, i.e., cost and weight constraints, under an uncertainty environment. In this work, fuzzy numbers have been adopted to overcome the problems related to the exact values of cost and weight of components used in system that are hard to obtain due to uncertainty in the real world. Next, the credibility theory is utilized to convert the fuzzy numbers to crisp numbers. In addition, the difficulty of the RAP encountered is the need to resiliently sustain the two nonlinear constraints, while also aiming to maximize system reliability by optimizing the redundancy allocation of components in parallel of each subsystem in the WSN. Hence, the model is solved by an improved simplified swarm optimization (ISSO). To prove the effectiveness of the ISSO in solving the researching model, experimental results are tested on random benchmarks. The experimental results show a significant efficiency and effectiveness of the proposed ISSO.