中文摘要：

无线传感器网络中基于声音能量的声源定位常采用最大似然估计法，该方法将定位问题转换为非线性函数的极值优化问题．本文提出一种文化一改进的量子粒子群优化算法（CMQPSO）解决这一非线性优化问题．首先，在量子粒子群（QPSO）的基础上，结合自适应变异思想和RSNTO算法，提出改进的量子粒子群算法（MQPSO）．然后，为了进一步改善算法的全局搜索能力、提高计算精度，利用文化算法的双重演化机制，将改进的量子粒子群算法纳入文化算法框架形成本文提出的CMQPSO算法．大量仿真实验表明，CMQPSO算法在全局搜索能力和收敛性能上较PSO、混合PSO．SNTO算法都有很大的提高；在解决声源定位上，CMQPSO算法与其他优化算法相比，定位精度有了明显提高．

英文摘要：

Maximum Likelihood Estimation is often used in the acoustic source localization based on energy in wireless sensor network , which transform localization problem into global optimization problem of nonlinear function. This paper presents a Cultural Modi- fied quantum particle swarm optimization algorithm （CMQPSO） to solve this nonlinear optimization problem. First, on the basis of the quantum particle swarm （QPSO） , combined with adaptive the variability ideas and RSNTO algorithm, the modified quantum par- ticle swarm optimization （ MQPSO } was proposed. Then, in order to further improve the global search ability and calculation accura- cy of the algorithm and according to the dual evolutionary mechanisms of the cultural algorithm, we integrated MQPSO algorithm into cultural algorithm framework to form a CMQPSO algorithm proposed in this paper. Simulation results demonstrate that CMQPSO al- gorithm can achieve robust convergence performance and better localization accuracy.