Parallelizing a Modified Particle Swarm Optimizer (PSO)

Bin Yang; Qi Lin Zhang

doi:10.4028/www.scientific.net/AMR.163-167.2404

Paper Titles

Application of Genetic Algorithm in Optimum Design of Structure
p.2381

Prediction of Parameters Error in Method of Construction Control in Cable-Stayed Bridge
p.2385

Dynamic Response Analysis and Optimal Design of a RC Slab to Blast Loads
p.2390

Shape Optimization and Seismic Design of Concrete-Filled Square Tubular Column and Reduced Beam Section with Cut Web
p.2397

Parallelizing a Modified Particle Swarm Optimizer (PSO)
p.2404

Research of Optimization Design on Floating Floor Insulation
p.2410

Multi-Target Optimization Design of Earthquake-Resistant Structure Taking Ductility and Cost into Account
p.2415

Optimization Design of Earthquake-Resistant Structure Taking Discrete Variables into Account
p.2420

Coefficient Distribution Method for Minimum Weight Design of Large-Span Determinate Trusses
p.2424

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 163-167Parallelizing a Modified Particle Swarm Optimizer...

Parallelizing a Modified Particle Swarm Optimizer (PSO)

Abstract:

Recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. Since this optimizer belongs to evolutionary algorithm and plagued by high computational cost as measured by execution time, in order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. The performance of the proposed algorithm is tested by two examples and promising speed-up rate is obtained. Final part is conclusion and outlook.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 163-167)

Pages:

2404-2409

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.163-167.2404

Citation:

Cite this paper

Online since:

December 2010

Authors:

Bin Yang, Qi Lin Zhang

Keywords:

Nonlinear Programming, Parallel Computing, Particle Swarm Optimization Algorithm (PSO), Truss Topological Optimization

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Nocedal and S.J. Wright. Numerical Optimization. Springer, (1999).

Google Scholar

[2] J. Kennedy and R. Eberhart, Particle swarm optimization, IEEE International Conference on Neural Networks, volume 4, (1995).

Google Scholar

[3] R.C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, In the Proceedings of the sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, pp.39-43, (1995).

DOI: 10.1109/mhs.1995.494215

Google Scholar

[4] P.J. Angeline, Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. LECTURE NOTES IN COMPUTER SCIENCE, p.601–610, (1998).

DOI: 10.1007/bfb0040811

Google Scholar

[5] B. Yang, Kai-Uwe Bletzinger, A modified particle swarm optimizer and its application to spatial truss topological optimization, In Proceedings of IASS 2009 Symposium, Valencia, Spain, (2009).

Google Scholar

[6] J.F. Schutte, B.J. Fregly, R.T. Haftka and A. George, A Parallel Particle Swarm Algorithm. In Proceedings of the Fifth World Congress of Structural and Multidisciplinary Optimization, Venice, Italy, (2003).

Google Scholar

[7] W. Achtziger and M. Stolpe. Truss topology optimization with discrete design variables-Guaranteed global optimality and benchmark examples. Structural and Mul-tidisciplinary Optimization, 34(1): 1–20, (2007).

DOI: 10.1007/s00158-006-0074-2

Google Scholar