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Topology optimisation of trusses can be formulated as a combinatorial andmulti-modal problem in which locating distinct optimal designs allowspractitioners to choose the best design based on their preferences. Bileveloptimisation has been successfully applied to truss optimisation to considertopology and sizing in upper and lower levels, respectively. We introduce exactenumeration to rigorously analyse the topology search space and removerandomness for small problems. We also propose novelty-driven binary particleswarm optimisation for bigger problems to discover new designs at the upperlevel by maximising novelty. For the lower level, we employ a reliableevolutionary optimiser to tackle the layout configuration aspect of theproblem. We consider truss optimisation problem instances where designers needto select the size of bars from a discrete set with respect to practice codeconstraints. Our experimental investigations show that our approach outperformsthe current state-of-the-art methods and it obtains multiple high-qualitysolutions.

Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems