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Ant colony system (ACS) is an algorithmic approach inspired by foraging behavior of real ants.
Ant Colony Optimization (ACO) is a multi-agent optimization algorithm inspired by the behavior of real ants.
Ant colony algorithms are a class of metaheuristics which are inspired from the behavior of real ants.
Ant colony optimization (ACO) is an ecological system algorithm, which draws its inspiration from the foraging behavior of real ants.
McLurkin, who believes that the greatest inspiration comes from observing nature, studied the behavior of real ants in ant colonies as a basis for his programming.
Ant Colony Optimization (ACO) is a meta-heuristic method based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of real ants.
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The artificial ants are used to mimic the behaviour of real ants and mark low energy ligand conformations with pheromone trails.
Ant Colony Optimization (ACO) is a Swarm Intelligence technique which inspired from the foraging behaviour of real ant colonies.
The inspiring source of Ant Colony Optimisation (ACO) is based on the foraging behaviour of real ant colonies [ 103, 104].
Ant Colonies optimization take inspiration from the behavior of real ant colonies to solve optimization problems.
The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design.
More suggestions(17)
behavior of real rats
behavior of real buildings
behavior of real cells
behavior of real devices
behavior of real people
behavior of real systems
behavior of real cats
behavior of real bees
behavior of individual ants
behavior of real ships
behavior of white ants
behavior of real gases
behavior of real proteins
behavior of real frogs
behavior of real components
behavior of real animals
behavior of real structures
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