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

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.

► Three efficient hybrid evolutionary algorithms based on integrating gradient-based local search. ► Approximate gradient calculated from design solutions of evolutionary algorithms. ► Integrated evolutionary algorithms and gradient directions without calculating gradients. ► Comparative performance of multiobjective evolutionary algorithms for truss design. ► Multiobjective design of 2D and 3D trusses with weight and compliance as objectives.