SCALE-INDEPENDENT BIOMECHANICAL OPTIMIZATION

Abstract : In this paper, the authors present a scale-independent method of optimization with a stochastic global optimization approach introduced by Kennedy and Eberhart: the Particle Swarm Optimizer (PSO). They apply this method to the biomechanical system identification problem of finding positions and orientations of joint axes in body segments through the processing of experimental movement data. They compare its performance to the BFGS optimizer, which falls under a class of optimizers more commonly used for this application. Traditionally, gradient-based methods such as the BFGS algorithm have been used to solve joint parameter identification problems, but major drawbacks to these methods are their sensitivity to problem scaling and algorithm parameter selection. These drawbacks require costly and time-consuming parameter sensitivity studies to be carried out for a problem before consistently acceptable results can be obtained. In addition, the presence of noise in the data will often cause premature convergence to an incorrect solution. The PSO method has some very desirable qualities that can be exploited in these types of problems. First, because it requires no gradient evaluations and because of the way it is formulated, the algorithm is insensitive to scaling of the design variables. Second, because of the algorithm's simplicity, there are very few parameters to tune, and even these have been shown to be relatively problem independent. Finally, the concurrent nature of the swarm algorithm lends it to parallelization, enabling the solution of problems that are too computationally challenging for single-processor machines. The need for greater computational power is common in the search for more realistic and accurate engineering models that currently can only be addressed by the use of parallel algorithms.