Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

Abstract

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.

Keywords:

• population-based incremental learning
• hybrid evolutionary algorithms
• multi-objective truss optimization
• comparative performance
• multi-objective evolutionary algorithms

Acknowledgements

The authors are grateful for the support of the Royal Golden Jubilee PhD Program (RGJ), the Thailand Research Fund (TRF) (grant no. BRG5580017) and the Sustainable Infrastructure Research and Development Centre (SIRDC), Khon Kaen University, Thailand.