基于改进遗传模拟退火算法的WSN路径优化算法*

为了更好地解决无线传感器网络（WSN）数据传输的路径优化问题，降低数据传输的能量消耗，提出了一种基于改进遗传模拟退火算法（SAGA）的WSN路径优化算法。首先根据优化目标建立数学模型，然后设计了种群的编码方式，并对遗传算法中的适应度函数、交叉算子、变异算子进行改进，使算法能够更加有效地避免陷入局部搜索；接着根据旧种群和新种群每个对应个体的不同进化程度提出了一种新的Metropolis准则，使模拟退火算法的跳变更具有规律性。实验结果显示：与其它路径优化算法相比，该算法不仅能生成更节能的数据传输路径，而且优化时间也大大降低。所以该算法是一种高效的路径优化算法。

WSN Path Optimization Algorithm Based on Improved Genetic Simulated Annealing Algorithm

In order to solve the problem of data transmission path optimization in Wireless Sensor Network(WSN) better and reduce the energy consumption of data transmission. A WSN path optimization algorithm based on improved Genetic Simulated Annealing Algorithm(SAGA) is proposed. Firstly, the mathematical model is established according to the optimization objective. Then, the encoding mode of the population is designed and the fitness function, crossover operator, and mutation operator are improved to make the algorithm more efficient to avoid falling into local search. And then a new Metropolis criterion are proposed according to the different evolution of each corresponding individual between the old and new population in order to make the jump change of Simulated Annealing Algorithm more regularly. Compared with other path optimization algorithms, the experimental results show that the algorithm can not only generate more energy-saving data transmission path,but also greatly reduce the optimization time. So, it is an efficient path optimization algorithm.