# A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations
## 3 Proposal and Methodology
### Multi-Agent System Design  
In this paper, we propose a multi-agent system (MAS) that integrates a genetic algorithm to obtain configurations for the localization of charging stations according to a utility function. The MAS is composed of a set of agents that offer services and provide flexibility, scalability, and reuse in other municipalities. The agents that participate in the MAS are the following (see Figure 1):  
* The Urban Agent: This agent is responsible for obtaining information about the amount of population in the different neighborhoods or blocks of the city under study. It extracts the information from census sections of Open Data portals. * The Traffic Agent: The agent's responsibility is to collect traffic information. With the information collected, the agent is able to answer queries about how much traffic there is on average in a certain defined area of the city. * The PoI Agent: The aim of the PoI Agent is to detect and classify points of interest (Pols) for the installation of future charging stations in the city based on its urban development plan. In addition, the agent carries out a clustering process to eliminate points of interest that are too close and to define zones of influence of the point with a minimum area. * The Popularity Agent: The task of the Popularity Agent is to determine the popularity of a point of interest based on the number of people who visit it and how much time those people spend in the area. To do this, it uses third party services, through an exhaustive search on the network, to locate this data. One example is Google's own service, which can be used to make a reverse resolution of coordinates to entities of interest on the map. Then, based on this information, the agent uses the results of the search engine to consult the estimated time spent by visitors in that area. * The Social Networks Agent: This agent retrieves geolocated information from social networks (Twitter, Instagram,...) to measure the popularity of a PoI based on the amount of activity that occurs through social networks in that area. * The Data Processing Agent: This module is in charge of aggregating all the information obtained by the previous collecting agents. The above mentioned collecting agents are each specialized in a specific type of information, while the Data Processing Agent is in charge of combining and completing all this information to send it to the next agent (the Emplacement Optimizer Agent) so that data analysis can begin. This agent could collect information from various collecting agents based on the information available in each city. * The Emplacement Optimizer Agent: This agent applies a genetic algorithm to determine the set of points of interest which are more appropriate according to the criteria that the user wants to optimize. * The User Interface Agent: this agent consists of a dashboard that will offer an interface for the criteria specification to be optimized, and information sources that are going to be taken into account by the Emplacement Optimizer Agent. This agent provides the visualization of the results of the Emplacement Optimizer Agent. * The system uses also a centralized database where all the persistence is done. This way, the Data Processing Agent and the Emplacement Optimizer Agent can share the computed data and the results, which are shown in the User Interface. The proposed MAS follows the next phases: (1) extraction of the set of points of interest (Pols) of the city \(P\) and their characterization considering different sources of information; (2) application of the genetic algorithm according to the conditions defined by the problem to be treated; and (3) visualization of the results. In the first phase, the PoI Agent determines the PoIs \(P\) for the location of a charge station \(s_{i}\). Initially, the agent considers public parking and garages to be PoIs.