Meta-Heuristics Algorithms based on the Grouping of Animals by Social Behavior for the Traveling Salesman Problem

In this paper, we show a survey of meta-heuristics algorithms based on grouping of animals by social behavior for the Traveling Salesman Problem, and propose a new classification of meta-heuristics algorithms (not based on swarm intelligence theory) based on grouping of animals: swarm algorithms, schools algorithms, flocks algorithms and herds algorithms: a) The swarm algorithms (inspired by the insect swarms and zooplankton swarms): Ant Colony Optimization algorithm – ACO (inspired by the research on the behavior of ant colonies), Firefly Algorithm (based on fireflies), Marriage in Honey Bees Optimization Algorithm MBO algorithm (inspired by the Honey Bee), Wasp Swarm Algorithm (inspired on the Parasitic wasps), Termite Algorithm (inspired by the termites), Mosquito swarms Algorithm – MSA (inspired by mosquito swarms), zooplankton swarms Algorithm ZSA (inspired by the Zooplankton) and Bumblebees Swarms Algorithm – BSA (inspired by Bumblebees). b) The school algorithms (inspired by the fish schools): The Particle Swarm Optimization algorithm – PSO (inspired by social behavior and movement dynamics of fish or schooling). c) The flock algorithms (inspired by the bird flocks): the flocking algorithm, and the Particle Swarm Optimization algorithm (inspired on the dynamics of the birds), d) The herd and pack Algorithms (inspired by the mammal herds and packs): bat algorithm (inspired by bat), wolf pack search algorithm WPS (inspired by wolfs), Rats herds algorithm RATHA (inspired by Rats), Dolphins Herds Algorithm DHA (inspired by Dolphins) and the feral-dogs herd algorithm FDHA (inspired by feral-dogs herd).