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（硕士论文）
2015六级真题遗传算法中适应度尺度变换与操作算子的比较研究
摘要
遗传算法是一种模拟生物界中自然选择和遗传机制的计算模型，其高度并行性、随机性、自适应全局优化概率搜索的优势引起了国内外的重视。遗传算法的理论和应用研究都取得了令人瞩目的进展，其应用成果已渗入到许多领域。但遗传算法的理论和操作方法尚未成熟，算法自身存在的一些不足还有待于进一步改进和完善。为此，本文分析了遗传算法的运行机理，对适应度函数和遗传算子进行了深入细致的研究。提出了一种改进的遗传算法：结合免疫浓度调节机制改进选择算子，采用嵌套次级遗传算法改进
交叉变异算子，并在C++环境中编程实现。最后，选择了一些典型的多维或高维复杂函数进行仿真测试，并将改进的遗传算法应用到PID参数的整定，通过仿真实验验证了本文提出的改进的遗传算法提高了算法的收敛速度和收敛概率，较基本遗传算法有明显的有效性和优越性。
关键词：遗传算法；适应度函数变换；遗传算子；函数优化；PID参数整定
A Comparison Study on Fitness Function scaling And Operators of Genetic Algorithm
ABSTRACT
Genetic Algorithm (GA) is a highly parallel, random and adaptive arching method bad on the mechanics of natural lection and genetic. The scholars of domestic and foreign pay much attention on the rearch of genetic algorithm’s theory and application, and have made an amazing progress. The achievement of genetic algorithm has permeated to a lot of fields. But the theory and method of genetic algorithm haven’t been mature yet. Some insufficiencies of algorithm are also waiting for further improvement and consummation. So, this paper analyzes the mechanism of genetic algorithm, studies the fitness function scaling and genetic operators. And an improved GA is designed: combining with immune concentration adjustment mechanism to improve lection operator, replacing crossover and mutation operator with nested condary genetic algorithm. The m
odified algorithm is realizes by programming with C++. Finally, the paper lects some typical multi-dimensional and high dimensional complex test functions to have a simulation test, applies the modified algorithm to the tuning of PID controller parameters, the simulations have demonstrated propod algorithms improve the convergence speed and the probability of convergence, approved the validity and superiority compared with the simple genetic algorithm.
Keywords: Genetic Algorithm; Fitness function scaling; Genetic operators; PID controller’s parameters tuning
（博士论文）顺利英语
群体智能算法研究及其应用
摘要
群体智能优化算法是一种近年来新兴的优化方法，是受到关注最多的优化研究领域之一，其模拟社会性动物的各种群体行为，利用群体中的个体之间的信息交互和合作来实现寻优的目的。与其它类型的优化方法相比，其实现较为简单、效率较高。
粒子群优化算法(Particle Swarm Optimization, PSO算法)源于鸟群和鱼群群体运动行为的研究，是一
种新的群体智能优化算法，是演化计算领域中的一个新的分支。它的主要特点是原理简单、参数少、收敛速度较快，所需领域知识少。具有量子行为的粒子群优化(Quantum‐behaved Particle Swarm Optimization, QPSO)算法是在深入研究PSO算法单个粒子收敛行为的基础上，受量子物理学的启发而提出，QPSO算法具有控制参数更少，收敛速度快，全局搜索能力强等特点。
本文以PSO算法与QPSO算法的理论分析及改进方法研究为重点，系统的研究了QPSO算法及其改进算法在相关方面的应用，具体内容如下:
(1)从最优化问题概念及其求解方法入手，阐述了智能智能优化算法研究背景，详细介绍了几种常见
的智能优化算法；通过阐述了没有免费午餐定理，说明了本文研究的基础；针对PSO算法的缺陷，提出了本课题的立题依据、研究目标、研究内容以及研究思路与方法。
(2) 首先介绍了Pso算法的基本原理与基本流程，详细讨论了两种重要的改进算法：带权重的Pso算法
和带压缩因子的Pso算法；阐述了QPSo算法的思想来源，给出了QPSo算法的设计思路。分析了
随机算法收敛的两个判断准则，即全局搜索算法的收敛准则与局部搜索算法的收敛准则，利用这
两个收敛准则作为依据，证明了QPSo算法是一个全局搜索的随机算法；对QPSo算法和Pso算法
从算法本身的角度做了比较，说明QPSo的特点；最后尝试在QPSo算法中引入一种新的变异机制，提出了基于云模型变异的量子粒子群优化算法(QPSO‐NCM)，从而增加种群的多样性，提高算法跳
出陷入局部寻优的能力，进一步增强全局搜索能力。变异操作能够增加群体的多样性，使得算法
congorialize具有突跳的能力，进入新的搜索区域。英语小故事100字
(3) 针对QPSO算法在解决多峰优化问题中也可能出现局部收敛的现象，分析了出局部收敛的主要原
因在于群体多样性较低而使得群体失去了在大范围内进行搜索的能力，通过使用物种形成策略的
概念，结合QPSO算法提出了一种SQPSO(The Species‐Bad QPSO)算法，将粒子群系统中的粒子
根据相似度进行划分，用来实现对多峰函数的优化。通过对静态多峰环境和动态多峰环境的测试
仿真证明，改进后的算法全局搜索能力和局部搜索能力均得到很大提升。
(4) 为了可以克服最小二乘法难于处理的时滞在线辨识，在QPSO算法中引入单神经元结构，提高算
法的局部搜索能力，实现线性离散系统的在线辨识。改进QPSO算法收敛速度快，窗口长度更小，更适用于实时要求比较高的在线辨识应用。在时变时滞系统在线辨识的仿真结果也验证了改进
我们结婚了中秋特辑QPSO算法具有很好的跟踪能力和稳定性，更适合实际的工程。通过引入接纳时间比控制机制，
提出并设计了一种基于QPSo算法在线辨识的自适应反馈控制方法，实现了动态调整Qos的性能
控制。
(5) 将QPSo算法分别用于混沌系统、周期系统和稳定系统中的参数辨识研究，通过仿真实验验证了
hardlyQPSo算法在系统参数辨识中比Pso算法和Ga算法具有更好的性能。对于存在噪声的混沌系统，提出基于QPSo算法的在线参数辨识并证明了该方法的有效性。
(6) QPSo算法在故障诊断方面的研究。智能故障诊断技术是人工智能和故障诊断相结合的产物，通
过人工的方法使用计算机模拟人类专家对复杂系统进行诊断。单一径向基(RBF)神经网络是一种性能良好的前向网络，其既有生物背景，又与函数逼近理论相配，适合于多变量函数逼近。用遗传算法优化RBF神经网络结构和权重等参数的方法具有一定的有效性，但遗传算法复杂的遗传操作(如选择、交叉、变异)使神经网络的训练时间随问题规模及复杂程度的增大而呈指数级增长。针对这些问题采用基于QPSO算法优化的RBF神经网络，进行故障进行诊断，可以有效地提高故障的正辩率。
论文最后对所做工作与主要研究成果进行了总结，并提出了进一步的研究方向。
recovery关键词：群体智能算法，优化技术，粒子群算法，量子粒子群算法，多峰优化，系统辨识，故障诊断
Study on the Swarm Intelligent Algorithm and Its Application
ABSTRACT
Swarm Intelligent (SI) algorithm is an algorithmic approach, which has gradually attracted more attention. To achieve the purpo of optimizing, SI simulated social behavior of various groups of animals and the individuals in the groups exchange information and cooperate each other. Compared with other optimization algorithms SI is easier to performe and more efficient.
Particle swarm optimization (PSO) is an evolutionary computation technique developed by Dr. Kennedy and Dr. Eberhart in1995, inspired by social behavior of bird flocking or fish schooling. PSO is simple in concept, few in parameters, and easy in implementation. It was proved to be an efficient method to solve optimization problems. Bad on the deep study of PSO algorithm and inspired by quantum physics, Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is propod. QPSO algorithm has much less parameters and much stronger global arch ability than the PSO algorithm.
Theoretical analys and algorithm improving on PSO algorithm and QPSO algorithm are mainly discusd in our work and the application of QPSO algorithm are also studied in this work. The main contents of this disrtation are as follows:
1. The concept of optimization problem and its solution are introduced to explain the rearch background of
the swarm intelligence algorithm and veral common intelligent optimization algorithms are described in detail. The basis of our study is illustrated by the no free lunch theorem. Against the defects of PSO algorithm, the rearch objectives, rearch content, rearch ideas and methods in the work are propod.
2. After the principle and procedure of PSO algorithm is prented, two important versions, PSO with inertia
weight and PSO with contraction coefficient, are discusd. Some improved PSO methods are also mentioned for reference. The thought of QPSO algorithm is discusd. Convergence criteria of random arch algorithms are introduced, including global convergence criteria and local convergence criteria.
Bad on the two convergence criteria, QPSO algorithm is proven to be a global arch stochastic algorithm. By comparing QPSO algorithm and PSO algorithm, the characteristics of QPSO are indicated.
Finally try to introduce a new mutation mechanism in the QPSO algorithm and the quantum-behaved particle swarm optimization bad on cloud model mutation (QPSO-NCM) is propod to increa the diversity of the population and improve the ability of the algorithm to fall into local optimization so as to enhance the global arch capability.
3. Premature convergence is also appeared in QPSO algorithm when solving multimodal problems. The
reason for premature convergence lies in the collections of swarm which makes the swarm diversity decline and the particles lo the ability of arching in a wide space. An improved Quantum-behaved Particle Swarm Optimization using the notion of species for solving multi-peaks functions optimization problems is propod. In the propod Species-bad QPSO (SQPSO), the swarm population is divided into paralleled species subpopulations bad on their similarity and each peaks are ensure to be arched equally, regardless if they are global or local optima. Our experiments for
static and dynamic multi-peaks environments demonstrate that global arch ability and local arch capabilities of the improved algorithm have been greatly enhanced.
4. In order to overcome the difficulty that the least-squares method cannot deal with time-delay-line
identification, QPSO algorithm combined with the single neuron is propod to improve the local arch capabilities and identification accuracy .Then the improved QPSO is applied to online identify parameters of a system described by differential equations. The improve QPSO algorithm has faster convergence speed and smaller length of the identification window, so it is more suitable for real-time online identification in practice. Time-delay and parameter changes for the simulation experiment illustrates the stability and tracking capability of improved QPSO algorithm are better. By introducing a ssion-bad admission time-ratio feedback control mechanism an adaptive control of Web QoS bad on system model online identification using QPSO algorithm is designed and implemented which dynamically adjust parameters of proportional-integral (PI) controller according to the changes of system model.
5. QPSO algorithm is ud to identify parameters of chaotic systems, periodic systems and stability systems.
The simulation results of QPSO compared with PSO and GA demonstrate that in the system parameter identification QPSO algorithm has best performance. For the existence of noi in chaotic systems, the online parameter identification bad on QPSO is propod and the effectiveness of the method is proved.女孩英语怎么说
6. The study of QPSO algorithm in fault diagnosis rearch. Intelligent fault diagnosis technology is a
combination of artificial intelligence and fault diagnosis, which u a computer to simulate human expert through artificial methods so as to diagnosis complex systems. A single radial basis function neural network (RBF NN) is a good performance feed forward network which not only has biological context, but also match with the function approximation theory and is suitable for multi-variable function approximation. It is validity to u GA to optimize the structure and weight parameters of RBF neural network. However the complexity genetic manipulation (such as lection, crossover, and mutation) of GA caus training time of the neural network increasing exponentially with the increa of the scale and complexity of the problem. To solve the problems, a RBF network algorithm bad on QPSO is prented to effectively improve faults diagnosis.
The main contributions in this work are summarized at last and further rearch considerations are put forward.
Keyword: Swarm intelligence, optimization technique, particle swarm optimization, quantum-behaved particle swarm optimization, multi-peaks optimization, system identification, faults diagnosis
