Hybrid Particle Swarm: Pattern Search Optimizer for Rocket Propulsion Applications

摘要

The ability of particle swarm optimization to locate global optimum solutions is combined with the usefulness of pattern search optimization in finding local optimum values to produce a robust tool for analyzing aerospace propulsion systems. Two aerospace applications are considered: designing a star-grain solid rocket motor to match specified thrust-vs-time curves and designing and optimizing a liquid-propellant missile system to specified constraints. For the first application, results are compared with those obtained from a regular particle swarm optimizer, a binary-encoded genetic algorithm optimizer, and a real-coded genetic algorithm optimizer. For the second application, results are compared with those obtained from a binary genetic algorithm. All optimizers are evaluated based on two criteria: fitness function accuracy, or how closely solutions meet a specified tolerance, and convergence speed, based on how many calls to the objective function are required to meet that tolerance.