Catalin Pruncu - Profile on Academia.edu (original) (raw)

Catalin Pruncu

Charles Camp related author profile picture

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Mehmet Saka related author profile picture

Bahador Mirzaei related author profile picture

Anikó Csébfalvi related author profile picture

nima Shokrolahi related author profile picture

Xin-She Yang related author profile picture

International Journal of Scientific Research in Science, Engineering and Technology IJSRSET related author profile picture

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Papers by Catalin Pruncu

Research paper thumbnail of Structure Optimization

The Big Bang − Big Crunch (BB− BC) optimization method is a recently developed meta-heuristic alg... more 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.

Research paper thumbnail of Structure Optimization

The Big Bang − Big Crunch (BB− BC) optimization method is a recently developed meta-heuristic alg... more 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.

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