《建筑能效智能计算方法设计—PSO算法应用》-毕业论文.doc

摘 要 所谓最优化问题，就是在满足一定的约束条件下，寻找一组参数值，以使某些最优性度量得到满足，即使系统的某些性能指标达到最大或最小。最优化问题的应用可以说遍布工业、社会、经济、管理等各个领域，其重要性是不言而喻的。 最优化问题根据其目标函数、约束函数的性质以及优化变量的取值等可以分成许多类型，每一种类型的最优化问题根据其性质的不同都有其特定的求解方法。 微粒群算法与其它进化类算法相类似，也采用“群体”与“进化”的概念，同样也是依据个体（微粒）的适应值大小进行操作。所不同的是，微粒群算法不像其它进化算法那样对于个体使用进化算子，而是将每个个体看作是在 n 维搜索空间中的一个没有重量和体积的微粒，并在搜索空间中以一定的速度飞行。该飞行速度由个体的飞行经验和群体的飞行经验进行动态调整。 关键字：最优化 群体 进化 微粒 微粒群算法  Abstract The so-called optimization problem is looking for a group of parameter values in satisfy certain constraints to make some optimality metric satisfied ;that is to make some of the performance index system reach a maximum or minimum .The application of optimal problem has been used throughout the industry, and social, Economy, management, etc .Its importance is self-evident .According to its target function optimization problem ,the properties and the restraint function optimization of variables can be divided into many types .Each type of optimal problem according to its property of difference has its particular solution . Particle swarm algorithm and the other evolution, clustering algorithm is similar also uses concept of the group and evolution , also based on a body (particles) fitness size operate. What is different, particle swarm optimization algorithm is not like other evolution algorithm for individual .Using evolutionary operators, but in each individual which is regarded as one of the search space dimension n no weight and volume of particles, and in the search space by a certain speed. This flight speed by individual flight experience and group dynamically adjust the flight experience. Keywords: optimization group evolution Particle Particle swarm algorithm  目 录 TOC \o 1-3 \h \z \u HYPERLINK \l _Toc20432 1. 绪论 PAGEREF _Toc20432 1 HYPERLINK \l _Toc27041 1.1 建筑智能计算 PAGEREF _Toc27041 1 HYPERLINK \l _Toc13141 1.2 面向建筑能效计算的智能计算 PAGEREF _Toc13141 2 HYPERLINK \l _Toc2422 1.3 本文结构 PAGEREF _Toc2422 2 HYPERLINK \l _Toc17228 2. pso算法的方法与原理 PAGEREF _Toc17228 3 HY