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Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13222))

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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.

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Authors and Affiliations

  1. University of Adelaide, Adelaide, Australia

    Hirad Assimi, Frank Neumann & Markus Wagner

  2. RMIT University, Melbourne, Australia

    Xiaodong Li

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  1. Hirad Assimi
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  2. Frank Neumann
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  4. Xiaodong Li
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Correspondence to Hirad Assimi .

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Editors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  2. Université du Littoral Côte d’Opale, Calais, France

    Sébastien Verel

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Assimi, H., Neumann, F., Wagner, M., Li, X. (2022). Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. In: Pérez Cáceres, L., Verel, S. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2022. Lecture Notes in Computer Science, vol 13222. Springer, Cham. https://doi.org/10.1007/978-3-031-04148-8_8

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  • DOI: https://doi.org/10.1007/978-3-031-04148-8_8

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  • Online ISBN: 978-3-031-04148-8

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