ISBN: 9783838398952 - enhanced routing in wireless network using ant colony optmization: wireless network (5 risultati)

Taschenbuch. Condizione: Neu. Enhanced Routing in Wireless Network using Ant Colony Optmization | Wireless Network | Priyanka Sharma (u. a.) | Taschenbuch | 104 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838398952 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Multimedia applications requires QoS based routing methodologies. Routing is an NP Hard Problem. Hence Artificial Intelligence techniques will be required to solve such problems. Ant Colony Optimization can be used to enhance routing using multiple QoS parameters. ACO is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Each ant is an autonomous agent that constructs a path. There might be one or more ants concurrently active at the same time. Ants do not need synchronization. Forward ants move to good looking neighbor from the current node probabilistically. Probabilistic choice is biased by pheromone trails previously deposited and heuristic function. Without heuristics information, algorithms tend to converge towards initial random solution. In backward mode, ants lay down pheromone. In ACO pheromone is added only to arcs belonging to the global best solution. Pheromone intensity of all the paths decreases with time, called pheromone evaporation. It helps in unlearning poor quality solution. After some time, the shortest path has the highest probability. 104 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multimedia applications requires QoS based routing methodologies. Routing is an NP Hard Problem. Hence Artificial Intelligence techniques will be required to solve such problems. Ant Colony Optimization can be used to enhance routing using multiple QoS parameters. ACO is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Each ant is an autonomous agent that constructs a path. There might be one or more ants concurrently active at the same time. Ants do not need synchronization. Forward ants move to good looking neighbor from the current node probabilistically. Probabilistic choice is biased by pheromone trails previously deposited and heuristic function. Without heuristics information, algorithms tend to converge towards initial random solution. In backward mode, ants lay down pheromone. In ACO pheromone is added only to arcs belonging to the global best solution. Pheromone intensity of all the paths decreases with time, called pheromone evaporation. It helps in unlearning poor quality solution. After some time, the shortest path has the highest probability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma PriyankaProf Priyanka Sharma is working as a Section Head for MTech ICT department at Institute of Technology, Nirma University. Currently she is doing her Phd in the area of Multimedia Networks and Ant COlony Optmization.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Multimedia applications requires QoS based routing methodologies. Routing is an NP Hard Problem. Hence Artificial Intelligence techniques will be required to solve such problems. Ant Colony Optimization can be used to enhance routing using multiple QoS parameters. ACO is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Each ant is an autonomous agent that constructs a path. There might be one or more ants concurrently active at the same time. Ants do not need synchronization. Forward ants move to good looking neighbor from the current node probabilistically. Probabilistic choice is biased by pheromone trails previously deposited and heuristic function. Without heuristics information, algorithms tend to converge towards initial random solution. In backward mode, ants lay down pheromone. In ACO pheromone is added only to arcs belonging to the global best solution. Pheromone intensity of all the paths decreases with time, called pheromone evaporation. It helps in unlearning poor quality solution. After some time, the shortest path has the highest probability.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…