Launch vehicle multi-objective reliability-redundancy optimization using a hybrid genetic algorithm-particle swarm optimization

This paper focuses on multi-objective reliability optimization of a two-stage launch vehicle using a hybridized Genetic Algorithm-Particle Swarm Optimization with provisions of relative weighting between the objectives. In this respect, the launch vehicle key subsystems as well as their functions are initially introduced. Subsequently, the system reliability block diagram is constructed using the launch vehicle working order of the subsystems augmented with the requirements for a robust fault/failure tolerant design and performance. Next, based on the proposed reliability block diagram arrangement a bi-objective optimization is formulated to maximize the system reliability while minimizing the launch vehicle cost with design variables being the component reliability, system redundancy level, and the type of components. To examine the validity of the proposed algorithm, a benchmark problem in reliability-redundancy optimization is also investigated through the proposed scheme. Benchmark simulation results indicate that neither genetic algorithm nor the particle swarm optimization alone can produce sufficiently accurate optimal results, but their hybrid combination is more efficient and achieves the desired level of accuracy. Thus the proposed hybrid genetic algorithm-particle swarm optimization is applied to the problem of launch vehicle multi-objective reliability optimization that has resulted in a convex Pareto front, out of which many design points could be selected for implementation. Finally, a Monte Carlo simulation is performed against the subsystem reliability uncertainty in order to analyze the effect of uncertainty on the total launch vehicle reliability and cost.