Abstract
AbstractRiver Formation Dynamics (RFD) is an evolutionary computation method based on copying how drops form rivers by eroding the ground and depositing sediments. In a rough sense, this method can be seen as a gradient-oriented version of Ant Colony Optimization (ACO). Several experiments have shown that the gradient orientation of RFD makes this method solve problems in a different way as ACO. In particular, RFD typically performs deeper searches, which in turn makes it find worse solutions than ACO in the first execution steps in general, though RFD solutions surpass ACO solutions after some more time passes. In this paper we try to get the best features of both worlds by hybridizing RFD and ACO, in particular by using a kind of ant-drop hybrid and considering both pheromone trails and altitudes in the environment.KeywordsRiver Formation DynamicsAnt Colony Optimization AlgorithmsHeuristic AlgorithmsNP-hard problems
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