Past research has shown that swarm intelligence (SI) techniques and fuzzy logic (FL) are two useful tools for solving engineering optimization problems in real-time applications. SI is an emerging concept of biologically inspired artificial intelligence based on the collective behavior of social colonies in nature. It studies decentralized and self-organized systems that can move quickly in a coordinated manner. As a group, simple creatures following easy rules can display a high degree of complexity and creativity. There are different types of SI, and their variants in the literature include crow search, social spider, cockroach swarm, bee colonies, hawk hunting, animal herding, bacterial growth, fish schooling, and microbial intelligence. Moreover, fuzzy logic (FL) has shown to be effective in controlling complex processes. Sometimes, even though the encoding scheme is compatible with the problem, the variables used in the FL do not utilize in favor of the problem such as membership function parameters and the number of clusters and initial centroid parameters in fuzzy c-means. Thus, parameter tuning approaches can be incorporated into the swarm-based techniques to handle the problems mentioned above. Similarly, SI algorithms often consume too much computation time due to the stochastic features of their searching approaches, slow convergence speed, and difficulty in achieving global optimal solutions in time.

For these reasons, there is a potential requirement to develop efficient techniques that are able to find solutions under limited resources, time, and money in real-world applications. Aiming at attacking these problems, adaptive fuzzy swarm optimization algorithms are proposed. This model uses the fuzzy system to adaptively adjust the number of agents and the random factors of the SI algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm.

This Special Issue will focus on examining how each of the SI and FL algorithms can be utilized for improving the performance of each other for real industry applications. This Issue will also discuss the capability of SI optimization techniques to obtain the optimal fuzzy system parameters. The above-mentioned topics are followed by tackling practical problems in medical applications, dynamic control, precision in agriculture, pattern recognition, multi-objective benchmarks, and signal processing. The Issue will provide novel guidance for relevant researchers and broaden the perspectives of fuzzy and SI researchers. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Fuzzy and type-2 fuzzy sets
• Swarm optimization algorithms
• Networks and communication systems, control systems, and energy systems
• Autonomous vehicles and vehicle routing problems
• Improved fuzzy rules for e-health decision support systems
• Fuzzy swarm intelligence for image and video analysis
• Fuzzy logic for swarm intelligence parameters optimization
• Swarm intelligence for fuzzy logic parameters tuning
• Fuzzy swarm intelligence in agriculture
• Fuzzy swarm intelligence in medicine
• Fuzzy swarm intelligence in pattern recognition and object tracking
• Swarm intelligence for adaptive neuro-fuzzy inference system (ANFIS) model adaption
• Swarm intelligence and fuzzy logic for classification/regression models
• Swarm intelligence for reinforcement learning optimization
• Fuzzy swarm intelligence for deep learning tuning parameters, in pattern recognition, and in dynamic control systems