Descrição: Tutorial de Geoprocessamento tendo como base aplicativo ArcGis/ Desktop 9.3. Desenvolvido por: Osvaldo José Ribeiro Pereira Graduado em Geografia pela UNESP e mestre em Ciências do Solo pela USP.
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Ant colony algorithm suffers drawbacks such as slow convergence and easy to trap into local optimum, therefore the path planning for mobile robot based on an improved ant colony optimization algorithm is proposed. The workspace for mobile robot is es
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Tutorial: ACO
1. Compare ACO and natural ants behavioral. In real world, ants wander randomly to find food and will carry the food to the nest as they find a food source. Ant will deposit pheromone along traveled path which is used by other ants to follow the trail. When one ant finds a good or short path from the colony to the food source, other ant will more likely to follow that path, and such the positive feedback will eventually leave all the ants following single path. Ant colony algorithm is a heuristic optimization method for shortest path and other optimization problems which borrows ideas from biological ants. The idea of ant colony algorithm is to mimic this behavior behavior with “simulated ants” walking around the search space representing the problem to be solved. Ant colony algorithms have been used to produce near-optimal solutions to the travelling salesman problem which is to find shortest path between n nodes.
2. Discuss ACO implementation in a TSP problem. Show each of the steps involved. The TSP is the problem of a salesman who wants to find the best path, starting from his home town, a shortest possible trip through a given set of customer cities and to return to its home town. In ACO algorithms, ants are simple agents which, in the TSP case, construct tours by moving from city to city on the problem graph. The ants’ solution construction is guided by (artificial) pheromone trails and an a priori available heuristic information.
