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).