Abstract
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.
Highlights
Node selecting is often used in all kinds of networks, including traffic networks, to help us select the most important nodes or edges
Reference [1] studied the node importance evaluation of the high-speed passenger traffic complex network based on the Structural Hole Theory
The proposed artificial slime mold can feed itself around the selected node by its propagation capability or spreading ability, such as Mexico City in the center of the traffic map in figure 4(a), and can form different topology connections in figures 4 (a)∼(p) around these highway nodes to be ranked
Summary
Node selecting is often used in all kinds of networks, including traffic networks, to help us select the most important nodes or edges. The propagation effect is decided by the network topology and the importance of a node selecting, namely the positions of different nodes or edges. To solve this problem, [3] used deep. An artificial slime mold is proposed to rank the network nodes by their propagation capabilities and topology importance. A comprehensive example is presented to test the proposed model, and the results reveal that the artificial slime mold can help us rank the traffic network nodes. The contributions of this paper are of both theoretical and practical importance for traffic network optimization and artificial intelligence theory
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