Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.