PSO-Based Cooperative Strategy Simulation for Climate Game Problem

The climate policy of game theory-based understanding could be used to find some insights about how players might implement different policies. To address this issue, the cooperative climate decision-making model by using agent-based simulation and optimization is established, and the solution of the non-cooperative climate game through particle swarm optimization (PSO) is developed in this paper. Firstly, learning agents are introduced to represent several players in climate game, evolutionary strategy using the decision-making and evaluation model based on individual interests and collective interests of Nash equilibrium is proposed. Then, the nonlinear fitness function of the PSO is designed, as well as the parameter selection and analysis. Finally, the Simulation experiments are performed by the nonlinear function and compared with Genetic Algorithm (GA). Experiment results showed that the proposed algorithm in this paper achieves the expected effect with fast response ability and the model can guide all agents to make a choice rationally in the process of non-cooperative game, so that the individual benefits and collective benefits reach the Nash equilibrium.