A Parallel Particle Swarm Optimizer
نویسنده
چکیده
1. Abstract Time requirements for the solving of complex large-scale engineering problems can be substantially reduced by using parallel computation. Motivated by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based global optimizer, the Particle Swarm Algorithm as a means of obtaining increased computational throughput. The Particle Swarm requires very few algorithmic parameters to define convergence behavior due to its simplicity, and, as a population based optimization method it is a natural candidate for concurrent computation. The parallelization of the Particle Swarm Optimization (PSO) algorithm is detailed and its performance and characteristics demonstrated for the biomechanical system identification problem as example. 2.
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