Evolutionary traveler's dilemma game based on particle swarm optimization

In this paper, we introduce the particle swarm optimization into the social dilemma, and investigate the effect of particle swarm optimization on the evolutionary traveler's dilemma game with the continuous version for different networks. In this modified model, each individual updates its strategy according to two terms, the most profitable strategy throughout its history and the imitation of the currently best strategy gained from its community. The simulation result reveals that the proposed learning method greatly facilitates the emergence and maintenance of cooperation in comparison with the traditional Fermi dynamics. Additionally, the particle swarm method makes an effective influence on the spatial interaction. When the reward/punishment is small, social interaction helps high-value claiming prevail in the system, leading to the diversity of population under a large strategy interval. And low-value claiming also benefits through frequent communication under a small strategy interval. Moreover, the cooperation level is enhanced by the increasing self-cognition at the expense of disorder and breakdown of community since each competing individual is able to gain more payoffs by adjusting its own superior strategy independently.