ANTS - Swarm Intelligence Ant-based algorithms Ref: Various...

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  B.Ombuki-Berman 1 Swarm Intelligence Ant-based algorithms Ref: Various Internet resources, books, journal papers (see assignment 3 references)
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  B.Ombuki-Berman 2 Ant-based algorithms
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  B.Ombuki-Berman 3 Swarm Intelligence Originated from the study of colonies, swarms of social organism Studies of the social behavior of organisms in swarms inspired the development of very efficient algorithms the foraging behaviour of ants resulted in ant colony optimization algorithms simulation studies of of bird flocks results in Particle swarm optimization A very recent and young field in computer science, with much potential ..lots of room for contributions!
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  B.Ombuki-Berman 4 Ant-based algorithms Ant-based systems are a population-based stochastic search methods. Sound familiar- it is similar to genetic algorithms There is a population of ants, with each ant finding a solution and then communicating with the other ants, how?
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  B.Ombuki-Berman 5 Ant System Swarm Intelligence Algorithm Based on real life animal swarms/groups Exhibit efficient ways to solve problems Ant System Developed by Marco Dorigo, 1991 Modeled after real life ant colonies, based on results of experiment by Goss
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  B.Ombuki-Berman 6 Ant System Experiment by Goss et al ’89 Ants started at nest Food placed some distance away Paths of different length between nest and food Ants found shortest path!
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  B.Ombuki-Berman 7 Ant Algorithms for shortest path Ants choose paths depending on pheromone After collecting food, paths are marked After some time, the shortest path has the highest probability
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  B.Ombuki-Berman 8 Ant System When ants travel they mark their path with substance called pheromone Attracts other ants When an ant reaches a fork in its path the direction it follows is based on amount of pheromone it detects Decision probabilistically made This causes positive feedback situation (i.e. Choosing a path increases the probability it will be chosen)
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  B.Ombuki-Berman 9 Ant algorithms We need to explore the search space, rather than simply mapping a route Ants should be allowed to explore paths and follow the best paths with some probability in proportion to the intensity of the pheromone on a given edge/trail. If the ants simply follow the path with the highest amount of pheromone on it, our search will quickly likely settle on a very sub- optimal solution
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  B.Ombuki-Berman 10 Ant algorithms The probability of an ant following a certain route is a function of both the pheromone intensity, and of what the ant can see. Furthermore, the pheromone trail must not build unbounded, hence evaporation is needed.
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  B.Ombuki-Berman 11 Ant System Group of ants start at home/nest An initial amount of pheromone already placed on edges Travel on edges Edges contain pheromone amount Visit nodes Probability of Selecting next node Based on distance between nodes and pheromone amount
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ANTS - Swarm Intelligence Ant-based algorithms Ref: Various...

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