Nature-Inspired Algorithms for Problem Solving

Abstract

Nature-inspired algorithms have emerged as powerful tools in the realm of problem-solving in the field of computational intelligence. These algorithms draw inspiration from the nature and apply them to optimization, learning, and decision-making tasks. One prominent example is genetic algorithms (GAs), modeled after the process of natural selection. GAs encode potential solutions to a problem as individuals within a population and use operators like selection, crossover, and mutation to iteratively evolve and refine these solutions over successive generations. This mimicking of evolutionary processes allows nature-inspired algorithms to efficiently explore solution spaces and discover optimal or near-optimal solutions. Swarm intelligence, another facet of nature-inspired algorithms, takes inspiration from the collective behavior of social organisms, such as ants, bees, and birds. Algorithms like ant colony optimization (ACO) leverage the power of collaboration and decentralized decision-making. Present research is focused on ACO for localization of sensor nodes and reducing the error rate.