Nature-inspired optimization algorithms are a recent topic of research and they are based on using of some nature-inspired behavior 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.

This 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 ultimate goal of exploring future research directions.

Potential topics include but are not limited to the following:

• Theoretical methods for understanding the behavior of nature-inspired methods
• Statistical methods for evaluating and parameterizing nature-inspired methods
• Novel nature-inspired or application-inspired optimization algorithms
• Statistical approaches for understanding the behavior of nature-inspired methods
• Optimization of Neuro-Fuzzy Models
• Optimization of type 2 fuzzy neural network models
• Optimization of emergent neural models with nature-inspired algorithms
• Fuzzy logic and intelligent and automatic control
• Flower pollination algorithm and cat swarm optimization
• An improved particle swarm optimization algorithm to optimize modular neural networks
• A fuzzy control design for an autonomous mobile robot using ant colony optimization
• Comparative study of type 2 fuzzy particle swarm, bee colony, and bat algorithms in optimization of fuzzy controllers
• Towards the self-adaptation of the bat algorithm
• Dolphin swarm algorithm
• Other nature algorithms and their applications