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Title: Ant colony optimization techniques for the vehicle routing problemAuthor: John E. Bell, Patrick R. McMullenYear: 2004 | Representation: finding the minimum distance or cost of the combined routes of a number of vehicles m that must service a number of customers n.Fitness Function: * Each customer is visited only once by a single vehicle. * Each vehicle must start and end its route at the depot, v0. * Total demand serviced by each vehicle cannot exceed Q.Performance:Ant colony optimization clearly has the ability to find good results within 1% of the known optimum for small problems. However, consistent with past research, the ACO methods used in this research are not as efficient in finding solutions for larger problems. | Title: Ant Colony System: A Cooperative LearningApproach to the Traveling Salesman ProblemAuthor: Marco Dorigo and Luca Maria GambardellaYear: 1997 | Representation:a set of cooperating agents called antscooperate to find good solutions to TSP’sFitness Fuction: * the state transition rule provides a direct way to balance between exploration of new edges and exploitation of a priori and accumulated knowledge about the problem * the global updating rule is applied only to edges which belong to the best ant tour * while ants construct a solution a local pheromone updating rule (local updating rule, for short) is applied.Performance:Once all the ants have generated a tour, the best ant deposits (at the end of iteration ) its pheromone, defining in this way a “preferred tour” for search in the following algorithm iteration t+1. In fact, during iteration t+1 ants will see edges belonging to the best tour ashighly desirable and will choose them with high probability. Still, guided exploration together with the fact that local updating “eats” pheromone away (i.e., it diminishes the amount of pheromone on visited edges, making them less desirable for future ants) allows for the search of new, possibly better tours in

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