Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems

TL;DR

This paper introduces a novel binary particle swarm optimisation method driven by novelty for truss topology optimisation, effectively exploring design spaces and outperforming existing methods in finding high-quality solutions.

Contribution

It proposes a new novelty-driven binary particle swarm optimisation approach combined with exact enumeration and evolutionary algorithms for improved truss design exploration.

Findings

Outperforms current state-of-the-art methods

Finds multiple high-quality solutions

Effectively explores large design spaces

Abstract

Topology optimisation of trusses can be formulated as a combinatorial and multi-modal problem in which locating distinct optimal designs allows practitioners to choose the best design based on their preferences. Bilevel optimisation has been successfully applied to truss optimisation to consider topology and sizing in upper and lower levels, respectively. We introduce exact enumeration to rigorously analyse the topology search space and remove randomness for small problems. We also propose novelty-driven binary particle swarm optimisation for bigger problems to discover new designs at the upper level by maximising novelty. For the lower level, we employ a reliable evolutionary optimiser to tackle the layout configuration aspect of the problem. We consider truss optimisation problem instances where designers need to select the size of bars from a discrete set with respect to practice code constraints. Our experimental investigations show that our approach outperforms the current state-of-the-art methods and it obtains multiple high-quality solutions.