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

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.