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An Improved Big Bang – Big Crunch Algorithm For Size Optimization of Trusses

Publish Year: 1391
Type: Conference paper
Language: English
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ICCE09_911

Index date: 28 September 2012

An Improved Big Bang – Big Crunch Algorithm For Size Optimization of Trusses abstract

The Big Bang–Big Crunch (BB–BC) optimization algorithm is a new optimization method that relies on the Big Bang and Big Crunch theory, one of the theories of the evolution of the universe. This method is among the heuristic population-based search procedures that incorporate random variation and selection,such as genetic algorithm (GA) and simulated annealing (SA). Alongside the main advantages of these methods, the problems resulting from the improper distribution of candidate solutions cannot be ignored,especially for high-dimensional functions. In this paper a method, namely Audze-Eglais’ approach, hasbeen applied to produce population that increases accuracy via homogeneous candidate solutions. Numerical results demonstrate the efficiency of the improved BB-BC method compared to other heuristic algorithm.

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An Improved Big Bang – Big Crunch Algorithm For Size Optimization of Trusses authors

Behrooz Hassani

Associate Professor of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

Mostafa Assari

Faculty of Civil Engineering , Islamic Azad University Kashmar Branch, Kashmar, Iran