Evolving High-Performance Evolutionary Computations for Space Vehicle Design

The nuclear electric vehicle optimization toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool currently uses a number of evolutionary computations (ECs) for designing NEP vehicles. Since evaluating candidate vehicle designs is computationally expensive, it is important that a set of robust control parameters be discovered. In order to accomplish this, a meta-genetic algorithm (meta-GA) was developed to discover control parameters for generational, steady-state, and steady-generational GAs as well as for particle swarm optimizers (PSOs) with ring, star, and random topologies. Our results show that the high-performance GAs are more efficient than the high-performance PSOs on a NASA asteroid mission problem.