针对无线多媒体传感器网络(WMSNs)中存在的覆盖盲区与覆盖重叠区域等问题,提出一种改进的量子遗传算法(QGA)来调整WMSNs中节点的位置和感知方向.相对于传统QGA,该算法采用从多条最优染色体构成的集合中随机选取优化目标来引导算法迭代,在保留算法收敛速度的同时改善其易收敛于局部最优的情况；同时,采用自适应旋转角和新的量子变异策略,加快算法的收敛速度.仿真实验表明:该算法具有良好的全局收敛能力和速度,可以更好地提高网络的覆盖率.%To improve the coverage blind spots and cover overlapping regions in wireless multimedia sensor networks (WMSNs) ,an improved quantum genetic algorithm(QGA)is presented to adjust the node location and the perceived direction of WMSNs. Compared with traditional quantum genetic algorithm, the algorithm randomly selects goals from the optimal genome team to guide algorithm iteration, reserveing the algorithm convergence speed as well as improve its easy-to converge to local optimum search capability, and meanwhile, adaptive rotation angle and new quantum mutation strategy are used to accelerate the convergence speed. Simulation experiment shows that the algorithm has good global convergence ability and speed,and can improve coverage of the network more efficiently
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