Bat Algorithm

The bat algorithm is a population-based metaheuristic optimization algorithm that models the echolocation behavior microbats use to hunt prey and navigate in the dark. Each virtual bat in the population is given a position representing a candidate solution together with a velocity, an emitted pulse frequency, a loudness and a pulse emission rate, and as the algorithm runs each bat adjusts its frequency and moves through the solution space, with loudness decreasing and pulse rate increasing as a bat gets closer to prey, mimicking real bats homing in on a target; a local random walk further refines the best solutions found so far, balancing the algorithm's exploration of new regions of the search space against exploitation of the good solutions it has already located. Xin-She Yang introduced the bat algorithm in 2010, as part of the same nature-inspired metaheuristic family as particle swarm optimization and cuckoo search.