Nature-Inspired Algorithms are computational methods that mimic natural processes to solve complex problems. These algorithms draw inspiration from various phenomena in nature, such as the behavior of animals, the growth of plants, and ecological systems. Examples include genetic algorithms, which simulate evolution, and particle swarm optimization, which models the social behavior of birds. These algorithms are widely used in fields like artificial intelligence, robotics, and operations research due to their ability to find optimal solutions efficiently. By leveraging the principles of natural selection, swarm intelligence, and other biological processes, they can tackle challenges in optimization, scheduling, and resource management.