基于遗传算法的社会网络移动模型

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.

A Genetic Algorithm-Based Mobility Model in Social Networks

Compared to traditional random mobility models, a social-based mobility model was proposed, aiming to generate synthetic traces to capture the statistical properties detected from real traces. the driving force of complicated social behaviors from an evolutionary point of view was explored. A genetic algorithm-based mobility model(GAMM) was presented. Using Gain/Cost Ratio as the metric of trace's fitness, complicated movement patterns were emerged from generations of evolutions. Explorer's model and transportation model were presented to show the expandability of GAMM. The influence of social-based mobility model on MANETs network protocols are also investigated by simulation.