Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm

In contrast with conventional structural optimization benchmark problems real size structures mostly contain a large number of members and their optimal design provide a serious challenging area for optimization methods. In this regard, current study deals with assessing the search performance of the recently developed fuzzy reinforced metaheuristic technique so called Interactive Fuzzy Search Algorithm on the optimization of large-scale structures. This method is a self-adaptive and parameter-free algorithm which applies a dual-module fuzzy decision mechanism to adjust its search behavior during the optimization process. This mechanism employs two nine rule fuzzy modules which permanently monitor the agents updating process and based on the governing conditions of the problem emphasize their exploration or exploitation search behavior. Attained results show that proposed method can adopt itself with the extensive search space of the studied problems. Form both accuracy and stability aspects Interactive Fuzzy Search Algorithm provides promising results on solving large-scale structural optimization problems.