中文摘要：

为了克服粒子群优化算法在复杂优化问题上易出现早熟收敛、多样性缺失等问题，提出了自适应子空间高斯学习的粒子群优化算法。该方法提出了适应值离散度和子空间高斯学习的概念，以自适应地调整参数和搜索策略，帮助粒子逃离局部最优。同时，该方法还提出邻域学习策略，引入了邻域最优粒子。当前粒子的邻城在进化过程中通过动态构建，以增强种群的多样性。实验对19个常用的经典基准测试函数在30和100堆进行了测试，结果表明该算法在收敛速度和寻优精度上优于一些知名的PSO算法。最后，将改进的算法应用于无线传感器网络覆盖优化问题。获得了较好的结果。

英文摘要：

In order to overcome the drawbacks of particle swarm optimization（PSO） ,which is easy to fall into local minima and lacks diversity, this paper proposes PSO algorithm with adaptive subspace Gaussian learning. The discrete degree of fitness and subspace Gaussian learning are employed to adaptively adjust parameters and search strategies, and helps the algorithm to jump out of the likely local optima. Moreover, we proposed neighborhood learning strategy in which the optimal neighborhood particle is introduced. The neighborhood of the current particle is dynamically constructed during the evolution, which increases the diversity of population. Experiments are conducted on 19 well-known benchmark functions with D = 30 and 100. The results show that our approach outperforms some recently proposed PSO algorithms in terms of convergence speed and solution accuracy. Finally, our approach is applied to wireless sensor network coverage optimization problem and obtains a promising performance.