Introduction to the Special Issue on Nature-Inspired Optimization Methods in Fuzzy Systems

THESE days, nature-inspired optimization methods are a wide range of the different algorithms which are very often used to solve complex optimization problems that cannot be efficiently solved by traditional optimization algorithms. Optimization of fuzzy systems is also a complex optimization task involving continuous, integer, and combinatorial problems. For example, selection of input attributes of the fuzzy system, design of fuzzy system structure, selection of membership functions, and selection of inference operators can be seen as combinatorial optimization problems, whereas selection of the parameters in membership functions and fuzzy rules are continuous optimization problem. In addition, optimization of a fuzzy system becomes a multiobjective optimization problems when we take both interpretability and accuracy of the fuzzy systems into account. Thus, applications of nature-inspired optimization methods and their hardware implementation are of great importance. It is our great pleasure to present this special issue of the IEEE TRANSACTIONS ON FUZZY SYSTEMS dedicated to “Nature-inspired optimization methods in fuzzy systems.” The special issue focuses on the development, adaptation, application, and hardware implementation of the methods inspired by nature for optimization of fuzzy systems. Thirty six articles have been submitted to our special issue, and based on the reviewers’ comments, 13 articles have been accepted for publication. These 14 accepted articles have been grouped into the four thematic sections based on the type of nature-inspired algorithm, which was used. These thematic sections are as follows: 1) evolutionary algorithms; 2) swarm intelligence algorithms; 3) hybrid nature-inspired optimization methods; 4) other nature-inspired optimization methods. We will give a brief introduction to the articles presented in this special issue according to the aforementioned four categories.