Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses

Abstract

This paper proposes a hybrid evolutionary algorithm for multiobjective optimisation of trusses using real-code population-based incremental learning (RPBIL) to solve multiobjective design problems. Differential evolution (DE) operators are integrated into the main procedure of RPBIL leading to a hybrid algorithm. The newly developed optimiser, along with some established multiobjective evolutionary algorithms (MOEAs) is implemented to solve a number of multiobjective design problems of trusses. Comparative performance based upon a hypervolume indicator shows that the new hybrid multiobjective evolutionary algorithm is superior to the other MOEAs particularly in cases involving large-scale truss design problems.