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A comparison of genetic algorithm and particle swarm optimisation for theoretical and structural applications

Conference Paper


Abstract


  • Genetic algorithms (GA) and particle swarm optimisation (PSO) are well-known for

    their ability in obtaining global optima. Some evidence exists in the structural

    engineering literature that PSO involves less overall computation effort than GA.

    Hence, these two methods have been selected and benchmarked against each other

    to test their relative robustness and efficiency for structural optimisation

    applications. This paper examines the performance and efficiency of these two

    optimisation algorithms in solving both mathematical benchmark functions and the

    classical ten-bar truss redundant problem. Tests are performed to assess the

    performance of each in relation to population size required and number of

    generations to achieve convergence. For the more complex problems, the PSO is

    shown to outperform the GA for smaller population sizes.

Publication Date


  • 2012

Citation


  • Wang, Z., McCarthy, T. J. & Sheikh, M. Neaz. (2012). A comparison of genetic algorithm and particle swarm optimisation for theoretical and structural applications. Eleventh International Conference on Computational Structures Technology (pp. 1-18). Stirlingshire, Scotland: Civil-Comp Press.

Scopus Eid


  • 2-s2.0-84893963908

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/803

Start Page


  • 1

End Page


  • 18

Place Of Publication


  • Stirlingshire, Scotland

Abstract


  • Genetic algorithms (GA) and particle swarm optimisation (PSO) are well-known for

    their ability in obtaining global optima. Some evidence exists in the structural

    engineering literature that PSO involves less overall computation effort than GA.

    Hence, these two methods have been selected and benchmarked against each other

    to test their relative robustness and efficiency for structural optimisation

    applications. This paper examines the performance and efficiency of these two

    optimisation algorithms in solving both mathematical benchmark functions and the

    classical ten-bar truss redundant problem. Tests are performed to assess the

    performance of each in relation to population size required and number of

    generations to achieve convergence. For the more complex problems, the PSO is

    shown to outperform the GA for smaller population sizes.

Publication Date


  • 2012

Citation


  • Wang, Z., McCarthy, T. J. & Sheikh, M. Neaz. (2012). A comparison of genetic algorithm and particle swarm optimisation for theoretical and structural applications. Eleventh International Conference on Computational Structures Technology (pp. 1-18). Stirlingshire, Scotland: Civil-Comp Press.

Scopus Eid


  • 2-s2.0-84893963908

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/803

Start Page


  • 1

End Page


  • 18

Place Of Publication


  • Stirlingshire, Scotland