An Individual Evolutionary Game Model Guided by Global Evolutionary Optimization for Vehicle Energy Station Distribution

Abstract

Collective decision-making problems consisting of individual decisions are commonly seen in social applications. In this article, the vehicle energy station distribution problem (VESDP) is considered, which is modeled as a network-based collective decision-making problem fulfilling consumers’ requirements by arranging the distribution of energy stations rationally. This problem involves the game among the government and energy station investors. The government intends to maximize the satisfaction of both gas and electric vehicle (EV) customers through policy guidance, while investors aim to maximize their own profits. To solve this problem, we propose an individual evolutionary game model guided by global evolutionary optimization with the following three features. From the individual perspective, we use a network-based evolutionary game with a confidence mechanism to describe the behavior of investors. From the global perspective, we design a genetic algorithm to find out the global-optimized program, which considers the satisfaction of all customers. To heal the divergence between these two perspectives, we design a policy formulation method for the government to motivate selfish investors to adopt strategies in accordance with the overall interests of all customers by using subsidies and taxation. Experiments are performed on both square grid and real-world networks. Experimental results demonstrate the effectiveness of the proposed model. IEEE