Transmission expansion planning is a problem of considerable complexity where classical optimization techniques are unable to handle large case studies. Decomposition and divide-and-conquer strategies have been applied to this problem. We propose an alternative approach based on agent-based modeling (ABM) and inspired by the behavior of the Plasmodium mold, which builds efficient transportation networks as result of its search for food sources. Algorithms inspired by this mold have already been applied to road-network design. We modify an existing ABM for road-network design to include the idiosyncratic features of power systems and their related physics, and test it over an array of case studies. Our results show that the ABM can provide near-optimal designs in all the instances studied, possibly with some further interesting properties with respect to the robustness of the developed design. In addition, the model works in a decentralized manner, using mostly local information. This means that computational time will scale with size in a more benign way than global optimization approaches. Our work shows promise in applying ABMs to solve similarly complex global optimization problems in the energy landscape.Energies 2019, 12, 4427 2 of 17 local, because they refer to agents and those they encounter in their proximity [3]. However, their interactions might lead to globally optimal design, such as in the cases of ant-colony behavior and slime mold growth, which have been shown to develop optimal networks [2]. In these biological systems, network development involves two phases: exploration and consolidation. The exploratory phase is characterized by the over-production of links between the core organism and the possible food sources. The consolidation phase, in turn, leads to the selection and positive reinforcement of the links that are used the most, while destroying or recycling the rest. The final network structure is likely to represent a context-specific balance between the need for efficient transport, low cost, and robustness.The inspiration for our algorithm comes from reference [2]. In their article, which is based on biological experiments, the authors argue that the slime mold Physarum polycephalum creates a network that is efficient enough to compete with the real design of the railway system around Tokyo. The molds Plasmodium and Physarum have been shown to be able to solve many computational problems, such as finding the shortest path between two points [4], constructing hierarchies of planar proximity graphs [5], or executing basic logical computing schemes [5,6]. The molds are also capable of sketching a mathematical model of an adaptive network that solves a convoluted maze [2]. The key underlying mechanism supporting its success, in all these instances, is always this cycle of over-production of links and consolidation. This is precisely what we will imitate in our algorithm: the transmission lines that are used the most will be reinforced, while those that are not used will be discarded. Some s...