Abstract

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.

Autore Pugliese

• Casavola Caterina , Lamberti Luciano

Tutti gli autori

• Casavola C , Lamberti L , Pruncu C

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2012

ISSN

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ISBN

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Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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