Due to the high complexity of real-world optimization problems, often it is not easy to solve them using traditional or deterministic optimization methods. During the last few decades, more than 150 novel nature-inspired algorithms and variants have been proposed in the current literature. Inspired by the behavior, experience, and cognition from nature and society systems, they aim to achieve satisfactory results for many difficult problems effectively and efficiently.

Such optimization algorithms mostly use stochastic operators and are gradient-free, which makes them suitable for solving many NP-hard problems for which exact methods are not efficient. Consequently, they have been successfully applied in a wide range of fields, including natural science, engineering, social science and humanities. Despite the success and popularity of nature-inspired intelligence, there are still some issues that require further research.

This Special Issue aims to promote the general advances of nature-inspired methods in terms of the schemes themselves. We look for the latest research papers on various aspects of nature-inspired algorithm as well as its potential use in various sciences and engineering domains. In addition to original research, we also welcome review articles.

Potential topics include but are not limited to the following:

• Convergence, scalability, and complexity analysis of nature-inspired algorithms
• Empirical performance and limitations of nature-inspired algorithms
• Hybridization and adaptation in nature-inspired algorithms
• Exploration/exploitation balance in nature-inspired algorithms
• Parallel and distributed nature-inspired algorithms
• Deep reinforcement learning for complex optimization
• Multi-objective optimization
• Multi-tasking optimization
• Discrete and combinatorial optimization
• Defense and cybersecurity
• Biometrics and bioinformatics
• Finance and economics
• Sport and games
• Novel real-world applications