An Improved Big Bang – Big Crunch Algorithm For Size Optimization of Trusses
Publish place: 9th International Congress on Civil Engineering
Publish Year: 1391
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICCE09_911
تاریخ نمایه سازی: 7 مهر 1391
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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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