An Improved Grasshopper Optimization Algorithm for Global Optimization

We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper optimization algorithm (GOA) is a recently proposed meta-heuristic algorithm inspired by the swarming behav-ior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we proposes a grasshopper optimization algorithm based on a logistic Chaos maps opposition-based learning strategy and cloud model inertia weight (CCGOA). CCGOA is divided into three stages. The chaos opposition learning initialization strategy is used to initialize the population, so that the population can be evenly distributed in the feasible solution space as much as possible, so as to improve the uniformity and diversity of the initial population distribution of the grasshopper algorithm. The inertia weight cloud model is introduced into the grasshopper algorithm, and different inertia weight strategies are used to adjust the convergence speed of the algorithm. Based on the principle of chaotic logistic maps, local depth search is carried out to reduce the probability of falling into local optimum. Fourteen benchmark functions and an engineering example are used for simulation verification. Experimental results show that the proposed CCGOA algorithm has superior performance in determining the optimal solution of the test function problem.     

基于粒子群算法的PID控制器参数优化

粒子群算法是一种智能算法,在PID控制器参数整定的应用中可取得更优的效果。为解决传统的粒子群算法早熟收敛和收敛速度慢的缺点,文中采用了一种基于相似度动态调整惯性权重的方法,即越靠近目前最优粒子的个体被赋予越小的惯性权重值。最后用MATLAB对等温连续搅拌釜反应器仿真。与标准的PSO算法整定方法相比,改进的粒子群算法稳定时间为230.1 s,比传统粒子群算法524.7 s的稳定时间缩小了一半,表明改进的算法对PID控制器的参数优化有着较优的收敛效果。     

一种解决约束优化问题的模糊粒子群算法

该文针对复杂约束优化问题,提出了一种模糊粒子群算法(FPSO),设计了一个新的扰动算子,在此基础上定义了模糊个体极值和模糊全局极值,利用这两个定义改进了粒子群进化的方程,利用该方程更新粒子的速度与位置,可以避免早熟收敛问题;定义了不可行度阈值,利用此定义给出了新的粒子比较准则,该准则可以保留一部分性能较优的不可行解微粒。用概率论的有关知识证明了算法的收敛性。仿真结果表明,对于复杂约束优化问题,算法寻优性能优良,特别是对于超高维约束优化问题,该算法获得了更高精度的解。     

一种求解连续对象优化问题的改进蚁群算法

蚁群算法在搜索过程中容易陷入局部最优解，且不适用于连续对象优化问题。文章针对这些问题．采用信息量变异、引入微粒群操作等方法进行改进，提出了一种引入微粒群操作的改进蚁群算法，并应用于求解连续对象优化问题。对几个典型复杂连续函数优化问题的测试研究表明，该改进算法不仅跳出局部最优解的能力更强．而且能较快地收敛到全局最优解，表明了算法的有效性。     

三目标柯西粒子群算法的电力系统无功优化

通过建立有功网损最小、电压偏差最小和静态稳定电压裕度最大的三目标无功优化模型。提出柯西粒子群算法,并针对IEEE14节点系统进行三目标电力系统无功优化。当种群多样性较差时,通过对交叉的粒子进行柯西变异从而扩大搜索空间,提高种群多样性,防止出现过早的收敛,进而避免了算法陷入局部最优解的问题,同时也提高了收敛速度。通过数据测试和比较柯西粒子群算法在收敛速度、精度、全局搜索能力上均优于常规差分进化算法和常规粒子群算法。其结果验证了该模型和算法的有效性,为电力系统安全经济运行提供了参考。     

优化工具在网优工作中的运用

一、GSM数据统计分析工具 随着网络规模的日益增大,日常的网络优化工作越来越重要。我们每时每刻都需要及时地跟踪网络整体运行性能,了解各项指标的走势,对出现问题的小区进行处理,针对TOPN小区进行优化。这些内容涉及到的数据都比较分散,有需要从亿阳网管上下载的,有需要从7200数据中获取的,对于综合分析网络性能比较麻烦。     

利用SIMULINK和优化工具箱实现PID参数优化

主要讨论了PID控制器参数优化问题。通过利用系统仿真模块与优化目标函数模块的结合，建立了SIMULINK模型，并利用优化工具箱方便地实现PID参数优化，从而提出了一种实现PID参数优化的新方法。仿真实验结果表明该方法简单、效果理想并且可靠性高。     

基于MySQL数据库的优化

总结现阶段MySQL数据库一些常见优化手段,论述了MySQL数据库存储引擎所适用的各类场合以及索引机制的优缺点,以及存储过程的优越性和硬件性能的重要性,在此基础上阐述了数据库切分技术,并提出分库的基本思路与方法,最后列出集群模型。     

FM-DCSK UWB接收机优化

基于搜索者优化算法（SOA,Seeker Optimization Algorithm）,首次将启发式搜索优化的理念引入到调频差分混沌键控超宽带接收机的积分时间最优化问题中。分析了积分时间对系统性能的影响,在IEEE 802.15.4a室内信道下得到给定信噪比的最优积分时间及其分布,同时也给出了最优积分时间并随信噪比变化的趋势。仿真结果表明,采用SOA较之传统方法更能高效地找到最优值。结论给该系统实现中的参数选择提供了有力依据,新理念的引入也将对难以用闭合数学模型表示的系统优化提供有益指导。     

Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization

Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. Several techniques have been developed and successfully applied for certain application domains. However, this work demands professional knowledge and expert experience. And sometimes it has to resort to the brute-force search. Therefore, if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method, it will greatly improve the efficiency of machine learning. In this paper, we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes. In this way, the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem. Bayesian optimization is based on the Bayesian theorem. It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function. A utility function selects the next sample point to maximize the optimization function. Several experiments were conducted on standard test datasets. Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models, such as the random forest algorithm and the neural networks, even multi-grained cascade forest under the consideration of time cost.     

多峰函数优化的黄金分割斐波那契树优化算法

针对多峰函数优化问题，基于斐波那契树优化算法，结合黄金分割思想，提出一种黄金分割斐波那契树优化算法.该算法利用斐波那契树优化算法全局局部交替寻优特性，通过在寻优过程中对优化问题解空间进行黄金分割比例压缩，从而提高算法局部搜索能力与小峰值搜索能力.多峰函数优化的仿真结果表明，该算法多峰优化能力强、速度快、精度高.     

Design and Optimization of Nonlinear Tapers using Particle Swarm Optimization

In this paper the design and optimization of a nonlinear diameter taper, connecting the output section of a gyrotron cavity to the uniform output waveguide section, is presented. The design of a nonlinear taper of a 42 GHz, 200 kW CW gyrotron operating in the TE_0,3 cavity mode with axial output collection has been taken as a case study. The taper synthesis has been carried out considering a raised cosine type of nonlinear taper and the analysis is done using a dedicated scattering matrix code. In addition, an improved particle swarm optimization - an evolutionary optimization - algorithm is used for the design optimization of this nonlinear taper. The optimum design of the taper shows the effectiveness of the presented method.     

弹簧的优化设计

本文叙述了在弹簧的设计中采用的优化设计方法的原因，弹簧优化设计的步骤,并列举了实例来阐述弹簧的优化设计。     

论GPRS网络优化

GPRS(通用分组无线业务)将移动通信与分组交换技术相结合,为移动客户提供因特网等数据通信业务,在近年来得到快速发展。简要介绍了GPRS网络优化特点,分析了优化原理,最后给出部分优化实例。     

三目标自适应变异微粒群算法的无功优化

电力系统无功优化是提高电能质量保证电网运行的重要环节,文中建立了综合考虑有功网损和电压偏移最小及电压稳定裕度最大的三目标无功优化模型,引入了自适应变异微粒群算法用于解决三目标电力系统无功优化问题。该算法利用群体的适应度方差来动态监控微粒群聚集的状况,采用增加随机扰动的方法对聚集的微粒进行变异,并对惯性权重进行自适应调整,使该算法既能跳出局部最优,防止早熟,又能提高收敛速度和精度。将该算法与其他算法应用于IEEE-14节点系统中进行无功优化,通过数据的计算和比较,结果验证了该模型和算法用于解决多目标电力系统无功优化问题的优越性和实用性。     

Optimization of Fiber Bragg Gratings Using a Hybrid Optimization Algorithm

A new hybrid optimization algorithm is proposed for the design of a fiber Bragg grating (FBG) with complex characteristics. The hybrid algorithm is a two-tier search that employs a global optimization algorithm (i.e., the staged continuous tabu search (SCTS) algorithm) and a local optimization method (i.e., the quasi-Newton method). First, the SCTS global optimization algorithm is used to find a "promising" FBG structure that has a spectral response as close as possible to the targeted spectral response. Then, a local optimization method, namely, the quasi- Newton method, is applied to further optimize the promising FBG structure obtained from the SCTS algorithm to arrive at a targeted spectral response. To demonstrate the effectiveness of the method, the design and fabrication of an optical bandpass filter are presented.     

GSM无线网络优化

GSM网络的不断扩容，频率复用越来越紧密，网络通话质量不断恶化，接通率下降，切换性能变差，另一方面，GPRS业务的推广应用，用户的消费意识与要求的不断提高，基于FDMA、TDMA的GSM网络的问题不断出现，因而GSM网络优化显得越来越重要，本文稿主要内容是以提高无线网络质量为目的的优化思路。     

基于生物地理学算法的稀疏平面阵列优化

天线阵列的相对旁瓣电平和阵元位置为非线性关系。对于非线性关系求最优值问题,用其他优化算法较难求解,而研究采用智能算法求解是一条值得探索的可行路径。提出了一种混合智能算法——改进生物地理学算法,即对生物地理学算法的迁移算子和变异算子进行改进,以提高种群的进化速率,进而优化对称和非对称稀疏平面阵列。仿真结果表明,与以往的算法相比,该算法提高了优化速率,降低阵列的相对旁瓣电平;阵元在阵列孔径内非对称分布比对称分布,所获得的旁瓣电平更低。     

基于三值多样性粒子群算法的MPRM电路综合优化

通过对离散三值粒子群算法的研究,提出一种三值多样性粒子群算法以求解MPRM（Mixed-Polarity Reed-Muller,MPRM）电路综合优化问题.首先根据混合极性XNOR/OR展开式的特点和几率换算法则,推导出三值粒子群算法的运动方程,在此基础上,采用广泛学习策略和三值变异操作进行算法改进;然后建立三值多样性粒子群算法的粒子与MPRM电路极性的参数映射关系,结合估计模型和XNOR/OR电路混合极性转换方法,将所提算法应用于MPRM电路的最佳功耗和面积极性搜索;最后对10个PLA格式MCNC Benchmark电路进行测试.结果表明：与已发表的方法相比,该文的优化算法表现出了总体显著性的性能优势.     

无约束最优化算法Rosenbrock的实现

不论对基础研究还是应用研究，优化问题都是一个普遍的问题，有广泛的应用背景。从现代的观点来看，优化问题的解决依赖于计算机强大的计算能力，其关键在于算法，实质就是一种搜索，是人工智能的关键技术之一，属于计算机科学的范畴。该领域是人工智能研究的一个热点，有大量的学者从事这方面的研究．并研究出多种不同特点的算法。首先介绍了其中的一种算法Rosenbrock，然后对其进行分析研究，并且编程在计算机上实现。