Fuzzy Metaheuristics: A State-of-the-Art Review

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies for developing heuristic optimization algorithms, reaching the optimum solution in the solution space more quickly by using efficient searches in a high-level work environment. There are various metaheuristics algorithms in the literature and the use of these algorithms for many problems in different areas is increasing rapidly. However, because the problems addressed are complex and uncertain, more effective and reliable results are needed for practitioners. In this context, fuzzy sets that better express uncertainties and reduce complexity can be successfully used with metaheuristic algorithms to achieve more concrete and realistic results. However, there isn’t a guiding source in the literature for those who want to research this topic. Therefore, this study aims to guide researchers on fuzzy-based metaheuristic algorithm applications by presenting literature research. For this aim, a large number of papers implementing fuzzy-based metaheuristic algorithms have been summarized with graphical figures by analyzing with respect to some characteristics such as subject area, published journal, publication year, and source country.