Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications

Abstract

Nature-inspired optimization algorithms are a recent topic of research, and they are based on using some natureinspired behaviors to solve optimization problems. Currently, a large number of approaches have been developed in this area, such as particle swarm optimization, bat algorithm, ant colony optimization, bee colony, dolphin algorithm, wolf search, flower pollination algorithm, and cat swarm. However, how to design efficient nature-inspired algorithms and how to use these algorithms for real-world application problems in control and robotics are still important issues. In particular, the design of neuro-fuzzy models, like type 2 fuzzy neural networks, type 1 fuzzy neural models, and intuitionistic fuzzy neural networks, has some current interest. In addition, new emerging neural models have been recently proposed. In all these models, a common problem is how to obtain an optimal structure, which can be handled by nature-inspired optimization algorithms. is special issue aims to bring researchers to report their latest research work on development of new nature-inspired algorithms or innovative applications of existing algorithms in the design of neural models for real-world applications in control and robotics, with an ultimate goal of exploring future research directions. In this special issue, we have five papers selected after a careful reviewing process. e five papers are representative of the current state of the art in this area.