Most of the metaheuristic optimization techniques are the general optimization tools with the ability of solving various class of problems. However, in the case of more complex engineering problems, adding extra customizing procedure(s) can improve their performance. In this regard, the current study deals with introducing a new auxiliary fuzzy decision mechanism to enhance these methods' capability on handling the structural size and topology optimization problems. The proposed mechanism aims to reduce the complexity level of the structural size and topology optimization problems by converting their complex search spaces into simpler fuzzy domains. Introduced fuzzy mechanism, during the optimization process, permanently monitors the population updating process and emphasizes either size or topology search behavior of each agent. Proposed mechanism evaluates agents via two predefined concepts so-called Normalized Objective Function (NOFi) and Normalized Members Density (NMDi). Since the presented fuzzy strategy designed as an independent regulator module, it can be integrated with different optimization algorithms. In the current work, it is combined with the Interactive Search Algorithm (ISA) optimization method and the compound method is named as Fuzzy Tuned Interactive Search Algorithm (FTISA). Eventually, its performance is comparatively assessed on solving a number of structural size and topology optimization problems with dynamic and static constraints. Achieved results demonstrate that the introduced fuzzy strategy not only significantly enhances the computational cost of the process but also improves the accuracy of the solutions and stability of the algorithm. (c) 2020 Elsevier B.V. All rights reserved.