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.
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.