基于免疫应答原理的人工免疫算法及其应用.doc

太原理工大学博士学位论文基于免疫应答原理的人工免疫算法及其应用姓名：续欣莹申请学位级别：博士专业：电路与系统指导教师：谢克明20090501 太原理工大学博士研究生学位论文（2）在人工免疫应答优化模型框架的基础上，提出了一种人工免疫应答优化算法（Artificial Immune Response Optimization Algorithm, AIROA）。定义了一组算子及其运算，按照亲和力大小将初始抗体群分配为记忆抗体群和一般抗体群，各自执行不同算子进行定义域变尺度的局部搜索和全局搜索，从而提高了进化后期的搜索精度，并加快了进化过程。对典型函数的数值优化实验表明该算法改善了克隆选择算法的性能，收敛速度更快，搜索精度更高。（3）提出了三种移动机器人的路径规划方法。针对基本人工势场法常常目标不可达问题，提出了一种基于人工势场法和遗传算法的移动机器人路径规划方法；针对遗传算法收敛速度慢、易陷入局部极值的问题，提出了一种基于人工免疫势场法的移动机器人路径规划方法；将 AIROA和人工势场法结合并用于移动机器人路径规划，具有更快的收敛速度和更高的搜索精度，产生的最优路径能使移动机器人可以安全地绕过障碍物且使得路径距离尽量短，从而达到路径规划的目的。（4）在人工免疫应答故障诊断模型框架的基础上，提出了一种基于变尺度检测器的人工免疫聚类算法（ Variable-sized Detectors Based Artificial Immune Clustering Algorithm, VDICA）。算法包含初始检测器的产生和检测器的聚类学习两部分，用蒙特卡罗方法对检测空间覆盖进行估计，避免了盲目确定检测器数目的行为；为检测器设定最大半径，使产生的检测器具有良好的分布特性；对算法参数进行了实验测取并分析了算法的可行性。（5）结合故障类型区域标记法，提出了一种基于 VDICA的故障诊断方法，用于机床齿轮箱的故障诊断中；结合 RBF神经网络，提出了一种基ii 太原理工大学博士研究生学位论文于 VDICA和 RBF神经网络的故障诊断方法，用于柴油机的故障诊断中。实验结果表明基于变尺度检测器的人工免疫聚类算法具有较强的检测空间覆盖能力和较高的聚类精度；基于 VDICA设计的故障诊断方法能实现复杂故障模式下的故障诊断，比基于 RBF神经网络的故障诊断方法具有高的准确率和更快的收敛速度。关键词：免疫应答，人工免疫应答优化，人工免疫聚类算法，路径规划，故障诊断iii 太原理工大学博士研究生学位论文IMMUNE RESPONSE PRINCIPLE BASED ARTIFICIAL IMMUNEALGORITHM AND ITS APPLICATIONABSTRACTAs a new generation natural computation method, artificial immune system (AIS) is a computing system for resolving all kinds of complicated problems, which is inspired by biological immune principle. AIS has been used in optimization, fault diagnosis, control and data processing and so on. It is becoming another research hotspot in the natural computation after artificial neural network, fuzzy system and evolutionary computation. AIS has two key problems, which are intrusion detection of system and optimization of internal learning principle of system. Performance of AIS can be improved if it has suitable computing model and algorithm. The research work of this dissertation belongs to the cross-frontier research field of life science, information science and computing science. Based on immune response principle, AIS is studied about resolving optimization problem and fault diagnosis problem in this dissertation. Research achievements of this dissertation can provide new ideas and methods for AIS. Furthermore, it plays important role in real optimization problem and mechanical fault diagnosis. The main work and innovative achievements of this dissertation can be concluded as follows: iv 太原理工大学博士研究生学位论文(1) Built a two-layer model framework of immune response on the basis of immune response process of biological immune system. According to mapping relation between biological immune system and artificial immune system, computation model framework of immune response was put forward. This model framework included two model frameworks. One is artificial immune response optimization model framework (AIROMF). The other is artificial immune response fault diagnosis model framework (AIRFDMF). (2) Proposed an artificial immune response optimization algorithm (AIROA) on the basis of AIROM. A series of operators and their calculation were defined. AIROA divided initial antibody population into memory antibody population and temporary antibody population with the affinity value. Different operators were executed to the two antibody populations as mentioned above for local searching and global searching with variable-sized domain. Searching precision in the later period of evolutionary was improved and accelerated. AIROA was used to optimize standard testing functions, and the results shew that it has faster convergence and higher precision and enhances the performance of CSA. (3) Put forward three path planning methods of mobile robots in the dissertation. Basic artificial potential field algorithm (APF) often gets into target and not reachable, so a new mobile robot path planning method based on APF and genetic algorithm (GA) was put forward. GA has slower convergence and gets into local minimum, so a mobile robot path planning method based on v 太原理工大学博士研究生学位论文artificial immune potential field was put forward. Based on AIROA and APF, a path planning method of mobile robot has quicker convergence and higher searching precision. It can make mobile robot safely avoid obstacles and quickly arrive the target. (4) Presented a variable-sized detector based artificial immune clustering algorithm (VDICA) on the basis of AIRFDM in the dissertation. VDICA includes the production of initial detectors and clustering learning of detectors. VDICA uses Monte Carlo method to estimate detectors space cover, so it does not need determine the numbers of detectors. Furthermore, it sets the maximum radius of detectors in order that detectors have better distribution characteristic. At last, algorithm parameters are determined by experiment. (5) Combining fault type field marker method, a VDICA based fault diagnosis method was put forward and applied for machine gearbox. A VDICA-RBF neural network based fault diagnosis method was put forward and applied for diesel engine. The experiment results showed that VDICA has better ability of detectors space cover and the fault diagnosis method based on VDICA which can realize fault diagnosis in complicated fault mode and has higher accuracy and quicker convergence than that of RBF. KEY WORDS: immune response, artificial immune response optimization, artificial immune clustering algorithm, path planning, fault diagnosis vi 太原理工大学博士研究生学位论文图 索 引图 1-1自然计算的框架3.3Figure1-1 Frame Work of Natural Computation3.3图 1-2否定选择算法流程图.5Figure 1-2 Flowchart of Negative Selection Algorithm .5图 1-3克隆选择算法流程图.7Figure 1-3 Flowchart of Clonal Selection Algorithm .7图 1-4本文主要研究内容与结构.14Figure 1-4 Frame Work of Dissertation.14图 2-1免疫应答.19Figure 2-1 Immune Response.19图 2-2免疫应答的两层模型框架.21Figure2-2 Two Layer Model Framework of Immune Response.21图 2-3人工免疫应答优化模型框架.24Figure 2-3 Artificial Immune Response Optimization Model Framework.24图 2-4人工免疫应答故障诊断模型框架.25Figure 2-4 Artificial Immune Response Fault Diagnosis Model Framework.25图 3-1人工免疫应答优化算法流程图.33Figure 3-1 Flowchart of Artificial Immune Response Optimization Algorithm.33图 3-2 AIROA中抗体群的状态转移过程.35Figure 3-2 Random Process of Antibody Population of AIROA.35图 3-3 Schaffer函数图像 .38Figure 3-3 Image of Schaffer Function.38图 3-4 Rosenbrock函数图像.38Figure 3-4 Image of Rosenbrock .38图 3-5 Branin rcos函数图像 .39Figure 3-5 Image of Branin Rcos Function .39图 3-6 De Jongs function函数图像 .40Figure 3-6 Image of De Jongs Function.40图 3-7 AIROA和 CSA对 Schaffer函数的优化 .41Figure 3-7 Optimization of Schaffer Function with AIROA and CSA.41图 3-8 Schaffer函数优化中算法的离线性能.41Figure 3-8 Off-line Performance of AIROA and CSA with Schaffer Function.41图 3-9 AIROA和 CSA对 Rosenbrock函数的优化.41Figure 3-9 Optimization of Rosenbrock Function with AIROA and CSA .41图 3-10 Rosenbrock函数优化中算法的离线性能.42Figure 3-10 Off-line Performance of AIROA and CSA with Rosenbrock Function .42图 3-11 AIROA和 CSA对 Branin rcos函数的优化.42Figure 3-11 Optimization of Branin rcos Function with AIROA and CSA.42图 3-12 Branin rcos函数优化中算法的离线性能.42Figure 3-12 Off-line Performance of AIROA and CSA with Branin rcos Function.42x 太原理工大学博士研究生学位论文图 3-13 AIROA和 CSA对 De Jongs函数的优化. 43Figure 3-13 Optimization of De Jongs Function with AIROA and CSA. 43图 3-14 De Jongs函数优化中算法的离线性能. 43Figure 3-14 Off-line Performance of AIROA and CSA with De Jongs Function . 43图 4-1目标点对移动机器人的引力场 . 47Figure4-1 Gravitation Field between Target and Mobile Robot . 47图 4-2障碍物的斥力场 . 47Figure 4-2 Excludability Field among Obstacles. 47图 4-3机器人、目标点和障碍物之间的合力场 . 48Figure 4-3 Resultant Force Field among Robot、Target and Obstacles. 48图 4-4机器人路径规划环境示意图 . 49Figure 4-4 the Eviroment of Robot Path Planning. 49图 4-5机器人赛场示意图 . 53Figure 4-5 the Field of Robot Game. 53图 4-6路径长度随进化代数变化的收敛曲线 . 53Figure 4-6 Convergence curve of path length with the evolution time. 53图 4-7 APFGA寻优得到的最短路径 . 54Figure 4-7 the Shortest Path APFGA Finds . 54图 4-8 APFGA寻优得到的第 3代最短路径 . 54Figure 4-8 the Path of the Third Generation of APFGA Finds . 54图 4-9 APFGA寻优得到的第 7代最短路径 . 55Figure 4-9 the Path of the Seventh Generation of APFGA Finds . 55图 4-10起始点到目标点的直线路径 . 55Figure 4-10 the Straight Path from Start to Target. 55图 4-11机器人路径规划坐标系示意图 . 57Figure 4-11 Map of Robot Path Planning Coordinate. 57图 4-12搜索避障区域示意图 . 57Figure 4-12 Map of Field Searched . 57图 4-13路径点选取示意图 . 58Figure 4-13 Map of Choosing Path Point . 58图 4-14路径抗体基因串示意图 . 58Figure 4-14 Map of Path-antibody Gene String. 58图 4-15简化后的路径抗体基因串示意图 . 59Figure 4-15 Map of Simplified Path-antibody Gene String. 59图 4-16基于基本人工势场法的路径规划 . 62Figure 4-16 Path Planning Based on Basic Artificial Potential Field . 62图 4-17基于基本遗传算法的路径规划 . 62Figure 4-17 Path Planning Based on Basic Genetic Algorithm . 62图 4-18基于人工免疫势场法的路径规划 . 62Figure 4-18 Path Planning Based on Artificial Immune P