Performance enhancement of multiobjective evolutionary optimisers for truss design using an approximate gradient

Abstract

This paper proposes hybridisation of evolutionary algorithms (EAs) and an efficient search strategy for truss optimisation. During an optimisation process, function gradients are approximated using already explored design solutions. The approximate gradient is then employed as a local search direction. The approximate gradient operator is integrated into the main search procedure of three multiobjective evolutionary algorithms (MOEAs) leading to three hybrid optimisers. The proposed hybrid strategies along with their original MOEAs are implemented on multiobjective design of truss structures. From the comparative results, it is found that the approximate gradient operator can greatly improve the search performance of MOEAs.