Slime Mold Inspired Evolving Networks under Uncertainty (SLIMO)

Slime molds have received much attention in the recent years. Ever since it was shown that the organism can solve network problems, researchers have worked to transfer the working principles to optimization algorithms. This paper introduces an innovative network evolving approach which enables the use for challenging tasks where information on the state of edges may be scarce, uncertain, and changing. We present a slime mold-based optimization algorithm (SLIMO) that integrates time-dependent changes of the uncertainty layer. In addition, we study the adaptation of the slime mold evolution and the corresponding single path or multi path solutions of the shortest path problem on a grid. Examples of potential applications include important topics in disaster and crisis relief and in sensor networks.