The Selfish Herd Optimizer (SHO)
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.
Cite As
Erik (2024). The Selfish Herd Optimizer (SHO) (https://www.mathworks.com/matlabcentral/fileexchange/64409-the-selfish-herd-optimizer-sho), MATLAB Central File Exchange. Retrieved .
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