Thesis Type: Postgraduate
Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Civil Engineering, Turkey
Approval Date: 2018
Student: REZA SALATIN
Supervisor: OĞUZHAN HASANÇEBİAbstract:
Despite the exceptional ability of the metaheuristics in locating the optimum solution of discrete optimization problems, the volume of the required analyses through these methods is burdensome. Optimization of large-scale structures having numerous load cases, as well as complex geometries, therefore, is not considered practical in construction industry. In current study, however, an effort has been made to pave the way for the industrialization of the structural optimization by enabling the traditional metaheuristics to efficiently solve the large-scale structural problems. Therefore, the so-called Convergence Rate approach is hybridized into two well-established optimization methods to reduce the computational effort of the metaheuristics. Initially, the newly proposed approach, which is named the Convergence Rate method, is developed and integrated into the Adaptive Dimensional Search and Exponential Big Bang-Big Crunch optimization methods. Through this method, not only have non-improving candidates been deleted from the optimization process, but the rest of the candidates within a population also have been excluded from the analyses once an improved solution within the expected range is unearthed from the same population. Thereafter, the proposed algorithm is tested on a large-scale onshore drilling rig structure having various load cases as well as a complex geometry. In the end, nonetheless the elimination of the vast majority of the candidate solutions from the analyses, the algorithm demonstrates a promising performance in locating the optimum solution of the problems.