The digital code that controls the movements of the birds mimics the actual behavior of a bird flock.
"The result is not," Anthony Lane writes, "an exercise in nostalgic regret, let alone in the lyrical ease that is promised by the title; just listen to one of the herders ranting, in a string of unsweet oaths, about the physical strains of the job and the ornery behavior of his flock".
All sheep have a tendency to congregate close to other members of a flock, although this behavior varies with breed, and sheep can become stressed when separated from their flock members.
The PSO algorithm is a population-based search approach, inspired by the behavior of birds' flocks [42] and has shown to be a simple and efficient algorithm compared to other search techniques, including for instance the widespread genetic algorithms [43].
The Tribes algorithm is inspired by the social behavior of bird flocking and is also an optimization adaptive procedure that does not require sociometric or swarm size parameter tuning.
Particle Swarm Optimization (PSO) is a nature-inspired computation technique based on the social behavior of birds flocking or fish schooling, and can be also viewed as a biologically inspired computational search and optimization method.
The proposed approach adopts a swarm intelligence paradigm inspired by the collective behavior of bird flocks.
PSO is a stochastic global optimization technique inspired by social behavior of bird flocking.
Particle swarm optimization (PSO) is a novel population based metaheuristic, inspired by social behavior of bird flocking or fish schooling.
PSO is a meta-heuristic algorithm, inspired by the social behavior of bird flocking, originally developed by Eberhart and Kennedy in 1995 [ 54].
