Evolutionary Optimization of the Gas/Charging Stations Topology for the Electric Vehicle Market

This study applies Evolutionary Algorithms (EAs) to optimize the profitability of a hybrid refueling network for conventional and electric vehicles. Three strategies are explored: reconfiguring existing stations, siting new ones, and combining both methods. A multiagent simulation models stakeholder interactions in a Spanish city, incorporating behavioral dynamics between users and operators. The approach features problem-specific objective functions, a compact encoding scheme, and a heuristic to reduce computational cost. Results show that small, strategically located mixed-use stations maximize profitability and support EV adoption.