Simultaneous topology, shape and sizing optimisation of a three-dimensional slender truss tower using multiobjective evolutionary algorithms

摘要

This paper presents an integrated design technique to carry out simultaneous topology, shape and sizing optimisation of a three-dimensional truss structure. Design objectives include mass, compliance, natural frequencies, frequency response function (FRF), and force transmissibility (FT). The Pareto fronts are explored by using: strength Pareto evolutionary algorithm (SPEA2), population-based incremental learning (PBIL), and archived multiobjective simulated annealing (AMOSA). The results obtained from using the optimisers are compared based upon the hypervolume (HV) and generational distance (GD). It is shown that PBIL is the best for optimising compliance and natural frequency, while SPEA2 is superior when dealing with FRF and FT.