The Selfish Herd Optimizer (SHO)

The Selfish Herd Optimizer (SHO) is a metaheuristic algorithm

The Selfish Herd Optimizer (SHO) is a metaheuristic algorithm which is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size.
More details can be seen in:

http://www.sciencedirect.com/science/article/pii/S030326471730103X

MATLAB SOFTWARE
The software contains a main function SHO_Example.m which implements the optimization of the Ackley function.