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.