Abstract
This study proposes a Heuristic Algorithm for Material Size Selection (HAMSS). It is developed to handle discrete structural optimization problems. The proposed algorithm (HAMSS), Simulated Annealing Algorithm (SA) and the conventional design algorithm obtained from a structural steel design software are studied with three selected examples. The HAMSS, in fact, is the adaptation from the traditional SA. Although the SA is one of the easiest optimization algorithms available, a huge number of function evaluations deter its use in structural optimizations. To obtain the optimum answers by the SA, possible answers are first generated randomly. Many of these possible answers are rejected because they do not pass the constraints. To effectively handle this problem, the behavior of optimal structural design problems is incorporated into the algorithm. The new proposed algorithm is called the HAMSS. The efficiency comparison between the SA and the HAMSS is illustrated in term of number of finite element analysis cycles. Results from the study show that HAMSS can significantly reduce the number of structural analysis cycles while the optimized efficiency is not different.
Highlights
There are many techniques used to handle structural optimization problem
In the Heuristic Algorithm for Material Size Selection (HAMSS) with Allowable Stress Design (ASD) 2005, the optimum steel volume of the structure is 83073 cm3 and it spends only 20 finite element analyses to get the answer. These results show that the HAMSS is more powerful than the Simulated Annealing Algorithm (SA) in term of number of finite element analyses
Since the proposed algorithm is developed by using knowledge of problem behavior, it is called the Heuristic Algorithm for Material Size Selection (HAMSS)
Summary
There are many techniques used to handle structural optimization problem. Chen and Su[6] suggested two methods to improve SA efficiency in optimal structural designs These referenced techniques can be used to handle structural optimization problem, large numbers of finite element analysis are needed to improve the result. The main problems of using huge number of finite element analysis cycles are that many rejected answers are generated. Are rejected because they do not pass constraints. These constraints, known as filters, involve member abilities to support both tension load and compression load. The typical optimized algorithm can be modified to reduce the computation time. Corresponding Author: Alongkorn Lamom, Faculty of Engineering, Chulalongkorn University, 10330, Thailand 943
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
