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 metagenetic algorithm (meta-GA) was developed to discover control parameters for generational, steady-state, and steadygenerational 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.