密集MANET下MPR的改进蚁群优化算法研究

Research on Improved Ant Colony Optimization Algorithm of MPR Under Dense MANET

针对传统多点中继(MPR)机制因使用贪心算法而导致求解集合冗余的问题,通过将蚁群优化算法与MPR机制相结合,提出一种基于状态信息的动态更新蚁群优化(DUACO)算法。与传统状态更新机制相比,该算法添加了信息素的动态更新机制和补偿-惩罚规则,考虑到节点移动性将会影响求解集合的精确度,重新定义蚁群算法中的路径选择函数,并将节点移动状态信息加入计算过程。实验结果表明,DUACO算法不仅能够有效降低MPR集合冗余以及提高网络性能,而且还可解决启发式蚁群算法易陷入局部最优解的问题。

The traditional Multi-Point Relay(MPR)mechanism uses the greedy algorithm,which usually leads to solution set redundancy.To address the problem,this paper combines the Ant Colony Optimization(ACO)algorithm and MPR to propose a Dynamic Update Ant Colony Optimization(DUACO)algorithm based on state information.Compared with the traditional state update mechanism,the algorithm introduces a dynamic update mechanism of pheromone and a compensation-penalty rule.Considering the node mobility affects the accuracy of the solution set,the path selection function in the ant colony algorithm is redefined,and the movement state information of the node is introduced into the calculation.Experimental results show that the DUACO algorithm not only significantly reduces the redundancy of the MPR set and improves network performance,but also avoids the tendency of the heuristic ant colony algorithm to fall into a local optimal solution easily.