Using Genetic Algorithms to Optimize the Location of Electric Vehicle Charging Stations

The creation of a suitable charging infrastructure for electric vehicles (EV) is one of the main challenges to increase the adoption of this new vehicle technologies. In this article, we present a Multi-Agent System (MAS) that performs an analysis of a set of possible configurations for the location of EV charging stations in a city. To estimate the best configurations, the proposed MAS considers data from heterogeneous sources such as traffic, social networks, population, etc. Based on this information, the agents are able to analyze a large set of configurations using a genetic algorithm that optimizes the configurations taking into account a utility function.

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