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.