【正文】 ......... 9 算法分析 .......................................................................................................................... 12 標(biāo)準(zhǔn)粒子群算法（ bPSO） ............................................................................................. 13 第 3 章 改進的粒子群優(yōu)化算法 ................................................................................................. 14 簡化粒子群優(yōu)化算法 ..................................................................................................... 14 關(guān)于 bPSO 中的粒子速度項的分析 .................................................................... 14 簡化粒子群優(yōu)化算法（ sPSO) .............................................................................. 15 sPSO 進化方程的收斂性能分析 .......................................................................... 15 帶極值擾動的粒子群優(yōu)化算法 ..................................................................................... 15 bPSO 收斂于局部極值的原因分析 ...................................................................... 16 帶極值擾動的粒子群優(yōu)化算法 ........................................................................... 16 帶極值擾動的簡化粒子群優(yōu)化算法 .............................................................................. 17 第 4 章 實驗及結(jié)果分析 ............................................................................................................. 18 標(biāo)準(zhǔn)測試函數(shù) .................................................................................................................. 18 實驗設(shè)計 .......................................................................................................................... 19 實驗結(jié)果及分析 .............................................................................................................. 19 固定 進化迭代次數(shù)的收斂速度和精度 ................................................................ 19 固定收斂精度下的迭代次數(shù) ............................................................................... 22 部分程序源代碼 ............................................................................................................. 22 第 5 章 基于粒子群 算法的 PID 參數(shù)優(yōu)化 ................................................................................. 26 PID 參數(shù)原理 ...................................................................................... 26 編碼和參數(shù)搜索空間 ............................................................................................ 26 優(yōu)化目標(biāo)和 步驟 .................................................................................................... 27 第六章 總結(jié)與展望 ..................................................................................................................... 28 總結(jié) .................................................................................................................................. 28 展望 .................................................................................................................................. 28 參考文獻 ....................................................................................................................................... 30 致謝 ............................................................................................................................................... 32 華北電力大學(xué)本科畢業(yè)設(shè)計（論文） 1 第 1 章 緒 論 優(yōu)化理論與方法是一門應(yīng)用性很強的學(xué)科，用于研究某些基于數(shù)學(xué)描述問題的最優(yōu)解。 關(guān)鍵詞 ： 粒子群優(yōu)化算法 ； 粒子速度；極值擾動 Comparative Study on Several Improved Particle Swarm Optimization Algorithms ABSTRACT Particle Swarm Optimization（ PSO） originally introduced by Doctor Eberhart and Kennedy is an optimization puting technology which derived from imitating the bird and fish flock’s praying behavior. It is a kind of optimization tool based on iterative putation. System initializes a group of random solution， then it searches the optimal solution through iteration ,and particles follow the optimal particle to run search in the solution space. The main trait of PSO is simple in principle ， few in tuning parameters ， speedy in convergence and easy in implementation． Now, PSO is used for training of neural works， optimization of functions and multitarget and it obtains good effect, its applied foreground is very wide． In itself, there are still a lot of defect in theory and practice． PSO develop towards the optimal solution’s direction depending on all the particles and its own particle’s search experience. In the later evolution, its convergence velocity bees slower. Meanwhile, its convergence precision is not high especially for the plex high dimensional multioptima optimization problems. The main works of the dissertation can be summarized as follows： (1)Reviewed some basic knowledge that relates to PSO, it’s mainly about the optimization problem and swarm intelligence. The PSO algorithm principles and flow are analyzed in detail. (2) Analysis the biological model of PSO and its evolution equation, particle velocity are not required. And if the particles’ velocity does not fit well, it may cause particles moving in the incorrect direction during evolution. Therefore put forward the simple PSO (sPSO) which only based on the position concept. The reason why the particles convergence in local extremum is that in the later evolution PSO cannot find the global optimal position. Put a random extremum disturbance on the individual and global extreme value, the extuemum disturbed PSO (tPSO) can overstep the local extremum. We put forward tsPSO, bined the sPSO and tPSO. (3) Briefly introduced the particle swarm optimization algorithm in the application of setting PID parameters. Key words: Particle Swarm Optimization。兩種策略結(jié)合，提出了帶極值擾動的簡化粒子群優(yōu)化算法。 （ 2） 分析粒子群算法的生物模型和進化迭代方程式，粒子速度概念不是必需的，粒子移動速度不合適反而可能造成粒子偏離正確的進 化方向，因此提出了只基于“位置”概念的簡化 粒子群算法。 論文的主要工作有： （ 1） 對研究 PSO 算法相關(guān)基礎(chǔ)知識進行回顧，主要是優(yōu)化問題和群體智能 。 但就其本身而言，在理論和實踐方面還存在很多不足 之處 。 它的主要特點是原理簡單、參數(shù)少、收斂速度較快、易于實現(xiàn)。 粒子群優(yōu)化算法及改進的比較研究 摘要 粒子群優(yōu)化（ Particle Swarm Optimization， PSO）算法是一種優(yōu)化計算技術(shù)，由 Eberhart 博士和