Multiple objective optimisation of composite sandwich structures for rail vehicle floor panels

This paper describes the application of an ant colony optimisation (ACO) algorithm to the multiple objective optimisation of a rail vehicle floor sandwich panel. The ACO algorithm was used to search a design space that was defined by sandwich theory and a material database in order to identify constructions that were optimal with respect to low mass and low cost. A broad range of mass and cost optimal sandwich material designs were identified successfully. These provided mass savings of up to 60% compared to existing plywood-based flooring systems, although mass savings above 40% had an associated cost premium.