Limitations of parallel global optimization for large-scale human movement problems.

Global optimization algorithms (e.g., simulated annealing, genetic, and particle swarm) have been gaining popularity in biomechanics research, in part due to advances in parallel computing.

Parallel asynchronous particle swarm optimization.

The high computational cost of complex engineering optimization problems has motivated the development of parallel optimization algorithms. A recent example is the parallel particle swarm optimization (PSO) algorithm, which is valuable due to its global search capabilities. Unfortunately, because existing parallel implementations are synchronous (PSPSO), they do not make efficient use of computational resources when a load imbalance exists.

Evaluation of a particle swarm algorithm for biomechanical optimization.

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.

Determination of patient-specific multi-joint kinematic models through two-level optimization.

Dynamic patient-specific musculoskeletal models have great potential for addressing clinical problems in orthopedics and rehabilitation. However, their predictive capability is limited by how well the underlying kinematic model matches the patient's structure. This study presents a general two-level optimization procedure for tuning any multi-joint kinematic model to a patient's experimental movement data.

Evaluation of parallel decomposition methods for biomechanical optimizations.

As the complexity of musculoskeletal models continues to increase, so will the computational demands of biomechanical optimizations. For this reason, parallel biomechanical optimizations are becoming more common. Most implementations parallelize the optimizer.