Volume 6, Issue 1 (1-2016)                   2016, 6(1): 101-114 | Back to browse issues page

XML Print


Abstract:   (3998 Views)

Due to the complex structural issues and increasing number of design variables, a rather fast optimization algorithm to lead to a global swift convergence history without multiple attempts may be of major concern. Genetic Algorithm (GA) includes random numerical technique that is inspired by nature and is used to solve optimization problems. In this study, a novel GA method based on self-adaptive operators is presented. Results show that this proposed method is faster than many other defined GA-based conventional algorithms. To investigate the efficiency of the proposed method, several famous optimization truss problems with semi-discrete variables are studied. The results reflect the good performance of the algorithm where relatively a less number of analyses is required for the global optimum solution.

Full-Text [PDF 801 kb]   (1918 Downloads)    
Type of Study: Research | Subject: Optimal design
Received: 2015/10/5 | Accepted: 2015/10/5 | Published: 2015/10/5