近年来，大型网络化系统的优化问题面临问题规模越来越大、目标函数构成越来越繁杂的局面。一方面，系统庞大导致传统的中心化算法容易面临算力不够的困境；另一方面，目标函数的构成复杂也导致利用分散式算法对大型系统进行拆分后，对不同的单个子系统的优化算法鲁棒性不足。本文提出了一种分散式的自适应粒子群算法（Adaptive Particle Swarm Optimization，APSO）有利于对多种拓扑结构的网络化系统进行优化，也适用于建筑的暖通空调系统（HVAC）的功耗优化问题。本文主要工作有以下三个方面：1、针对粒子群算法在解决较复杂大规模系统优化问题时，容易陷入局部最优，难以得到最优解的情况，基于在迭代过程中对惯性权重和学习因子进行动态自适应调整，提出了一种自适应粒子群算法。并且在不同基准问题的测试中与另外三种算法进行了对比，自适应粒子群算法在单峰函数和多峰函数上都有很好的表现。2、提出了分散式的自适应粒子群算法，利用相邻子系统决策变量对子系统粒子群的搜索过程进行引导。在环状拓扑结构以及网状拓扑结构中，针对不同节点中的不同基准问题，对分散式自适应粒子群算法的性能进行了验证，并与集中式自适应粒子群算法的求解结果进行了比较。 3、针对建筑的暖通空调系统（HVAC）建立分散式模型，并利用分散式自适应粒子群算法优化系统的能耗，调整系统的运行策略。并与已有序优化算法以及分布式分布估计算法进行比较，仿真实验表明，本篇文章中提出的分散式自适应优化算法对系统节能的效果良好。

In recent years, optimization problems in large scale networkedsystems have faced a situation of increasing problem scale, and The objective function is becoming more and more complicated. On the one hand, the large scale of the systems are making centralized optimization algorithms easy to face the the dilemma of insufficient computing power. On the other hand, the complex composition of the objective function also makes it more difficult to optimize the various subsystems after the large-scale system is divided by the distributed algorithm. This thesis provides decentralized Adaptive Particle Swarm Optimization (APSO) for these problems. APSO can deal with optimization problems with different characteristics, and Decentralized algorithms are conducive to the optimization of large scale networked systems. The contributions are as follows: 1．An adaptive particle swarm optimization algorithm that dynamically and adaptively adjusts inertia weights and learning factors is proposed, to solve the search ability ?problem of particle swarm optimization. And compared with the other three algorithms in the test of 13 benchmark problems. APSO can solve more complex optimization problems, and it has good performance on both unimodal and multimodal function.2. A decentralized adaptive particle swarm algorithm is proposed, which uses neighboring information to guide the search process of the subsystem particle swarm. In the ring topology and the mesh topology, the performance of the decentralized APSO is verified for different benchmark problems in different nodes. Then we ?analyzed the results of centralized PSO and decentralized PSO.3. We establish the decentralized model for Heating, ventilation and air conditioning(HVAC) systems. And we use the decentralized adaptive particle swarm algorithm to optimize the energy consumption of the system and adjust the operating strategy of the system. We compare our method with other existing methods.