Multi-objective Architecture Optimization Based on Evolutionary Algorithm with Grid Decomposition

The design of safety-critical systems must concern both cost and availability. However, the design space of redundancy is large with increasing system scale and complexity. Achieving optimal configurations that balance availability and cost can be difficult in the large design space. Therefore, we propose an optimization method for system architectures using the multi-objective evolutionary algorithm based on constrained decomposition with grids (MOEA-CDG). Firstly, a bi-objective model is defined and the availability is calculated on the basis of the discrete-time Bayesian network (DTBN). Then, MOEA-CDG is used to achieve the optimal configurations that meet both cost and availability. Finally, the proposed method is illustrated with an example of the Integrated Modular Avionics (IMA) core processing system, and the results indicate that the method can improve the efficiency of architecture design and outperforms elitist non-dominated sorting genetic algorithm (NSGA-II).