基于遗传算法的小世界网络一致性速度优化

研究了一类具有小世界网络拓朴结构的连续时间动态特性的复杂网络的一致性问题.应用遗传算法对小世界网络进行优化和计算机仿真,结果表明:对于小世界网络而言,网络达到一致性所需要的时间随着网络中捷径的数量和捷径连接的具体节点的不同而显著不同.小世界网络中捷径数量增加,网络系统达到一致性的时间将变短.对于小型的小世界网络,在捷径数量一定的情况下,可以通过遗传算法,对每一条捷径连接的节点进行优化选择,从而使小世界网络的一致性收敛速度加快.

Optimization of consensus speed to small-world network based on genetic-algorithm

In this paper,the consensus problem for a class of continuous-time dynamical networks with small-world topologyis investigated.By the optimization of the small-world network topology and genetic-algorithm and simulations,it is found that,for a small-world network,the time to reach a consensus changes rapidly with the change of the number of shortcuts and the nodes which shortcuts connected with.Furthermore,the consensus time becomes shorter as the number of shortcuts increased.For a small-world network with smaller network size and fixed shortcuts,we can optimize the nodes that each shortcut connects using genetic-algorithm methodology.By this way,Thge small-world network obtained has faster consensus convergence speed.