Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently- developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm’s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms—a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units.
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e-mail: fregly@ufl.edu
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June 2005
Technical Papers
Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization
Jaco F. Schutte,
Jaco F. Schutte
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
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Byung-Il Koh,
Byung-Il Koh
Department of Electrical and Computer Engineering
University of Florida
, Gainesville, FL 32611-6250
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Jeffrey A. Reinbolt,
Jeffrey A. Reinbolt
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
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Raphael T. Haftka,
Raphael T. Haftka
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
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Alan D. George,
Alan D. George
Department of Electrical & Computer Engineering,
University of Florida
, Gainesville, FL 32611-6250
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Benjamin J. Fregly
Benjamin J. Fregly
Department of Mechanical & Aerospace Engineering and Department of Biomedical Engineering,
e-mail: fregly@ufl.edu
University of Florida
, Gainesville, FL 32611-6250
Search for other works by this author on:
Jaco F. Schutte
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
Byung-Il Koh
Department of Electrical and Computer Engineering
University of Florida
, Gainesville, FL 32611-6250
Jeffrey A. Reinbolt
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
Raphael T. Haftka
Department of Mechanical & Aerospace Engineering,
University of Florida
, Gainesville, FL 32611-6250
Alan D. George
Department of Electrical & Computer Engineering,
University of Florida
, Gainesville, FL 32611-6250
Benjamin J. Fregly
Department of Mechanical & Aerospace Engineering and Department of Biomedical Engineering,
University of Florida
, Gainesville, FL 32611-6250e-mail: fregly@ufl.edu
J Biomech Eng. Jun 2005, 127(3): 465-474 (10 pages)
Published Online: January 31, 2005
Article history
Received:
July 9, 2003
Revised:
January 1, 2005
Accepted:
January 31, 2005
Citation
Schutte, J. F., Koh, B., Reinbolt, J. A., Haftka, R. T., George, A. D., and Fregly, B. J. (January 31, 2005). "Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization." ASME. J Biomech Eng. June 2005; 127(3): 465–474. https://doi.org/10.1115/1.1894388
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