对于无线传感器网络(WSNs)中的两大关键性问题路由搜寻和能量优化,引入量子遗传算法进行路径的搜寻,并改进算法编解码思路,降低由于网络规模扩大而导致编码长度急速增加,即减少算法的计算复杂度,从而解决传统编码方式下的量子遗传算法难以适用于大规模的WSNs的缺点.通过实验表明:该方法能够得到更加优越和稳定的路径搜索结果,与粒子群优化算法进行1000次重复路径搜寻试验比较,其平均最优解提高了 18.9％,稳定性提升了38.9％.%There are two key problems in wireless sensor networks( WSNs) which are routing search and energy optimization. The improved quantum genetic algorithm ( QGA) is used to search optimal route and proposed an energy-saving strategy to improve energy consumption. In traditional encoding, while network scale spread, the algorithm encoding length increased quickly. Meanwhile, the algorithm' s computational complexity will be much higher. So the encoding is improved to solve these problems, and made the routing algorithm available for large-scale WSNs. Simulation shows that the improved QGA can get more superior and stable routing path. Comparing to particle swarm optimization, this method increased by 18. 9 % in average optimal solution and improved by 38. 9 % in stability after 1000 repeated simulation.
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