A Node Selecting Approach for Traffic Network Based on Artificial Slime Mold

The node selecting problem of traffic network is a significant issue and is difficult to be solved. In this paper, an artificial slime mold method is proposed to help us solve the problem. First, the chief components of an artificial slime mold are introduced to simulate the foraging behavior of a true slime mold, including external food sources, plasmodium, myxamoeba, nucleus, and nutrients. Then the learning mechanism of nutrient concentration for the artificial slime mold is illustrated, though there is no brain or neuron in its body. After that, the node selecting approach is described according to the propagation capabilities of nodes. Second, the algorithm flow is designed to show how to solve this kind of complex selecting problem. The algorithm flow to select important traffic nodes by artificial slime mold is composed of 4 main steps, including initialization, food searching, feeding, and selecting for output. Third, a comprehensive example is designed and derived from references to certificate that the proposed artificial slime mold can help us select important traffic nodes by their generated traffic topologies. The contributions of this paper are important both for traffic node selecting and artificial learning mechanism in theoretical and practical aspects.

Abstract licence: CC BY-NC-ND

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