Weight minimization of truss structures with Big Bang-Big Crunch

The Big BangBig Crunch (BBBC) optimization method is a recently developed meta-heuristic algorithm that mimics the process of evolution of the universe. BBBC has been proven very efficient in design optimization of skeletal structures but yet computationally more expensive than classical meta-heuristic algorithms such as genetic algorithms and simulated annealing. To overcome this limitation, the paper presents a novel hybrid formulation of BBBC where the meta-heuristic search is hybridized by including gradient/pseudo-gradient information as a criterion to perform new explosions. Each new trial design is formed by combining a set of descent directions and eventually corrected in order to improve it further. The new BBBC algorithm is successfully tested in two classical weight minimization problems of a spatial 25-bar truss and a planar 200-bar truss.