The Big BangBig Crunch (BBBC) optimization method is a recently developed meta-heuristic algorithm that mimics the process of evolution of the universe. BBBC 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 BBBC 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 BBBC algorithm is successfully tested in two classical weight minimization problems of a spatial 25-bar truss and a planar 200-bar truss.
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