A Parallel Particle Swarm Optimizer

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2003
Accession Number
ADA466417

Entities

People

  • A. D. George
  • B .j. Fregly
  • J. F. Schutte
  • R. T. Haftka

Organizations

  • University of Florida

Tags

Communities of Interest

  • C4I
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computers
  • Coordinate Systems
  • Data Sets
  • Engineering
  • Errors
  • Identification
  • Image Recognition
  • Operating Systems
  • Optimization
  • Particle Swarm Optimization
  • Particles
  • Prosthetics
  • Reliability
  • Surgery
  • Test And Evaluation
  • Topology Optimization

Readers

  • Aerospace Engineering
  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.