Hybrid Particle Swarm: Pattern Search Optimizer for Rocket Propulsion Applications

The ability of Particle Swarm Optimization (PSO) to locate global optimum solutions is combined with the usefulness of Pattern Search Optimization (PS) in finding local optimum values to produce a powerful tool for analyzing aerospace propulsion systems. Two aerospace applications are considered: (1) design a star grain solid rocket motor (SRM) to match specified thrust vs. time curves; and (2) design and optimize 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 (GA) optimizer, and a real code genetic algorithm optimizer. For the second application, results are compared with those obtained from a binary GA. All optimizers are evaluated based on two criteria: (1) “fitness function” accuracy, or how closely solutions meet a specified tolerance, and (2) convergence speed, based on how many calls to the “objective function” are required to meet that tolerance.

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