Nature-Inspired Optimization

Nature-Inspired Optimization refers to techniques that mimic natural processes and behaviors—like animal foraging, evolution, or swarm movement—to find the best solutions to complex problems. These methods generate and refine options iteratively, inspired by how nature solves similar challenges efficiently. For example, algorithms modeled after ants find the shortest path to food sources, or genetic algorithms imitate evolution to improve solutions over generations. By drawing on nature's problem-solving strategies, these methods can effectively explore large, complicated search spaces to identify optimal or near-optimal solutions in various fields, from engineering to logistics.