Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability

Frequency constraint structural optimization includes the exploration of highly nonlinear and non-convex search spaces with several local optima. These characteristics of the search spaces increase the possibility of the agents getting trapped in a local optimum, when using a meta-heuristic algorithm.In this paper a diversity index is introduced which together with a few other criteria, can be employed to recognize such traps. By the use of these concepts, a hybridization of the Charged System Search and the Big Bang-Big Crunch algorithms with trap recognition capability is proposed. Five numerical examples are considered to demonstrate the efficiency of the algorithm.

► Frequency constraint optimization is performed for highly nonlinear and non-convex search spaces. ► A hybridization of the CSS and BB-BC algorithms is proposed. ► A diversity index is introduced which is employed for trap recognition. ► Five numerical examples are presented to demonstrate the efficiency of the algorithm.