Pigeon-Inspired Optimization (PIO)
Pigeon-Inspired Optimization (PIO) algorithm is a new swarm intelligence algorithm inspired by the homing behaviors of pigeons, proposed by Haibin Duan and Peixin Qiao in 2014.
PIO is as simple as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms, and uses only common control parameters such as map and compass factor. PIO as an bio-inspired optimization tool, two operators are designed by using some rules.
In map and compass operator, pigeons can sense the earth field by using magnetoreception to shape the map in their brains. They regard the altitude of the sun as compass to adjust the direction. As they fly to their destination, they rely less on sun and magnetic particles.
In landmark operator, when the pigeons fly close to their destination, they will rely on landmarks neighbouring them. If they are familiar to the landmarks, they will fly straight to the destination. If they are far from the destination and unfamiliar to the landmarks, they will follow the pigeons who are familiar to the landmarks.