Optimization Algorithm Performance in Determining Optimal Controls in Human Movement Analyses

[+] Author and Article Information
R. R. Neptune

Human Performance Laboratory, University of Calgary, Calgary, AB T2N 1N4 Canada

J Biomech Eng 121(2), 249-252 (Apr 01, 1999) (4 pages) doi:10.1115/1.2835111 History: Received September 03, 1997; Revised September 13, 1998; Online January 23, 2008


The objective of this study was to evaluate the performance of different multivariate optimization algorithms by solving a “tracking” problem using a forward dynamic model of pedaling. The tracking problem was defined as solving for the muscle controls (muscle stimulation onset, offset, and magnitude) that minimized the error between experimentally collected kinetic and kinematic data and the simulation results of pedaling at 90 rpm and 250 W. Three different algorithms were evaluated: a downhill simplex method, a gradient-based sequential quadratic programming algorithm, and a simulated annealing global optimization routine. The results showed that the simulated annealing algorithm performed far superior to the conventional routines by converging more rapidly and avoiding local minima.

Copyright © 1999 by The American Society of Mechanical Engineers
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