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与传统的随机移动模型相比,社会网络移动模型旨在生成更符合实际数据统计规律的移动场景.为了从进化的角度研究复杂行为产生的原因,提出了基于遗传算法的移动模型(GAMM),使用“社会收益”与“移动开销”之比作为衡量节点运动轨迹环境适应性的准则,使复杂的移动特性在简单的进化过程中涌现出来.为证明GAMM具有较高的扩展性,提出探索者模型和交通工具模型来满足不同场景的需要,并通过一个网络仿真的实例来研究社会网络移动模型对移动自组织网络路由协议性能的影响.
Compared with the traditional stochastic moving model, the mobile model of social network aims to generate a mobile scenario that is more in line with the statistics of the actual data.In order to study the causes of the complex behavior from an evolutionary perspective, a moving model based on genetic algorithm (GAMM) Using the ratio of “social income ” to “moving cost ” as a criterion to measure the adaptability of the node’s motion trajectory environment, complex mobility features emerge in a simple evolutionary process.In order to prove that GAMM has high scalability , Proposed the explorer model and the vehicle model to meet the needs of different scenarios and through a network simulation example to study the impact of social network mobile model on the performance of mobile ad hoc network routing protocol.