基于微粒群算法的资源均衡问题研究

为了更有效地解决工程项目管理中的资源均衡问题,将微粒群算法与工程项目资源均衡问题相结合,建立基于微粒群算法的工程项目资源均衡模型.其中对微粒群算法的相关参数进行了研究,对工程项目资源模型进行具体分析与设计,并采用计算机仿真设计方法.通过实例进一步验证基于微粒群算法解决资源均衡问题的可靠性和有效性.研究发现,一定程度上,它在解决资源均衡问题时较传统方法更为简单,参数设计与选择较容易,且取得了更优的结果.     

基于加速粒子群算法的车辆座椅悬架最优控制研究

针对传统最优线性二次型控制器中加权矩阵往往由设计者根据经验确定的问题,提出一种应用加速粒子群算法确定加权矩阵的方法。建立"车轮-车身-座椅、人体"6自由度随机振动系统模型,采用加速粒子群算法对座椅悬架进行参数优化,并对优化后系统进行最优线性二次型控制。将基于加速粒子群算法的最优线性二次型座椅悬架系统中"座椅、人体"垂向加速度与初始系统及基于常规粒子群算法和遗传算法的最优线性二次型控制系统进行对比,验证了此控制系统的有效性和优越性。     

基于微粒群优化算法确定爆破测震参数

介绍了微粒群算法的基本理论,并把微粒群优化算法应用于爆破测震参数的计算中,结合实际工程,应用微粒群优化算法对爆破震动参数进行确定,得到了非常好的拟合效果.微粒群优化算法应用于爆破参数的确定是切实可行的.     

基于微粒群优化的模型参考自适应控制

针对复杂非线性对象提出了一种基于微粒群优化(PSO)的PID自适应控制方法.通过运用PSO算法对PID控制器参数进行在线调整,使模型参考自适应控制达到理想的控制效果.将该方法引入到连续搅拌反应釜这一复杂的非线性系统,仿真结果表明了该方法的良好性能.     

潜艇自航模试验系统可靠性冗余优化

可靠性设计水平直接影响潜艇自航模系统设计的成败.在某潜艇自航模任务与系统构成分析的基础上,开展了某潜艇自航模系统的可靠性分析,应用多阶段任务系统可靠性模型理论建立了该系统的任务可靠性分析和计算模型,综合潜艇自航模的设计约束条件,并结合满意度的概念,建立了系统可靠性冗余优化模型且利用微粒群算法求解.示例证明了模型的可用性及可信性.     

SVM和改进的PSO在压路机驾驶室结构噪声优化中的应用

针对压路机驾驶室结构噪声,将拉丁超立方试验设计、支持向量机近似模型、改进的粒子群优化算法相结合,通过修改驾驶室主要板件的板厚参数降低压路机结构噪声。建立一套基于支持向量机和粒子群算法控制车内结构噪声的设计流程。针对粒子群可能出现局部最优解的问题,对粒子群进行了改进。并利用改进的粒子群优化支持向量机参数,构建高拟合精度的支持向量机模型代替有限元模型。并用改进的粒子群算法对该模型进行板厚寻优,找到一组最佳的板厚参数使得参考点(驾驶员右耳处)声压级最小,减少计算工作量,提高优化效率。     

强横风作用下车辆半主动悬架模糊PID控制研究

为提高汽车高速行驶时的抗横风性能,提出一种阻尼系数修正的半主动悬架控制方法。基于Carsim 和MATLAB平台建立整车动力学模型和A级路面模型,设计一种基于横风的半主动悬架模糊PID控制系统,并利用粒子群算法优化PID控制器参数。PID控制器依据车辆质心垂直加速度确定各轮悬架系统的基本阻尼系数,模糊控制器根据横风强度和方向对阻尼系数进行修正。通过MATLAB和Carsim 联合仿真和实车道路试验与被动悬架系统相比,经过半主动悬架控制后的质心垂直加速度峰值和标准差均下降30 %以上,侧倾角速度标准差下降25 %以上,车辆在强横风作用下的行驶平顺性和安全性得到有效提高。     

工程项目多目标协同优化研究

将微粒群算法(particle swarm optimization,PSO)引入工程项目多目标协同优化领域,研究工程项目的质量、费用、资源和工期的协同优化问题。文章首先系统介绍微粒群算法原理、流程以及算法的改进发展,然后研究了工程项目质量、费用、工期和资源的协调功效系数,并建立了质量、费用、工期和资源的多目标协同优化模型,接下来介绍了应用微粒群算法编码解决工程项目多目标优化的方法步骤。最后,通过一个应用实例,计算表明微粒群算法可以准确快速地解决工程项目多目标协同优化问题。     

气动调节阀最优分数阶PID控制器设计EI北大核心CSCD

针对工业控制过程气动调节阀阀位控制中非线性,模型不精确等问题,提出一种基于分数阶PID控制器(fractional order PID controller, PI~λD~μ)的阀位控制方法。分析气动调节阀工作原理并建立其数学模型,为提高模型准确性,针对分数阶PID控制器参数整定范围广、复杂性高等问题,提出一种改进量子粒子群算法(improved quantum particle swarm optimization, IQPSO)整定分数阶PID控制器参数,引入混沌映射和非均匀高斯变异增强算法寻优能力,将改进算法用于调节阀控制系统模型辨识。仿真与试验结果表明,相比于整数阶PID控制器,所设计的分数阶PID具有更快的响应速度和控制精度,能更好地满足气动调节阀阀位控制要求。     

建筑结构振动分散控制系统的多目标优化研究

基于结构可控性指标、粒子群—差分进化多目标混合群(MOHO)算法及分散控制系统结构随机响应求解提出了一种分散控制系统多目标优化设计方法,对结构分散控制系统的作动器位置、数量、控制器参数和子系统划分进行优化。利用结构可控性指标对作动器最优布置楼层进行确定;利用多目标混合群优化算法对分散控制系统内各子系统作动器数量和控制器反馈增益进行优化;以随机地震激励下反映结构振动控制效果和控制策略优劣的双指标作为各子控制系统的优化目标函数。针对一12层框架结构,采用提出的分散控制系统多目标优化方法对多种分散控制方案进行有优化设计,并在实际地震动激励下,对优化结果进行时程验证分析,表明该优化方法是稳定、有效可行的,优化得到的分散控制系统较集中控制系统而言有更理想的控制效果。     

基于改进PSO算法的结构损伤检测

结构的损伤检测常转化为求解约束优化问题,针对粒子群算法容易出现早熟问题,增大算法后期的粒子位置的改变量,从而增加粒子位置的差异,因而能够增强其在求解约束优化问题时抵抗局部极小的能力。两层刚架单损伤和多损伤识别的数值结果和收敛曲线表明了改进后的粒子群算法优于传统的带惯性因子的粒子群算法。三层框架结构的4种损伤工况的试验研究进一步说明了该算法应用于结构损伤检测领域的有效性。     

基于智能混合策略的测试集优化

利用基于粒子群和蚁群算法的智能混合优化策略,删除冗余测试向量以解决测试集的优化问题. 利用蚁群算法的并行搜索能力构造初始解集,通过粒子群优化算法将解集维数降低,确定每次迭代的个体最优解和全局最优解,并利用新粒子信息更新信息素,最终通过多次迭代找到一个或多个最优测试集. 通过多组数据实例分析可知: 该智能混合优化策略与蚁群算法等其他测试集优化算法相比,可得到多个可行性最优测试集;与蚁群算法相比可提高收敛速度,并降低蚁群算法参数选取对收敛结果的影响,从而避免次优解的出现.     

PSO Based Multi-Objective Approach for Controlling PID Controller

CSTR (Continuous stirred tank reactor) is employed in process control and chemical industries to improve response characteristics and system efficiency. It has a highly nonlinear characteristic that includes complexities in its control and design. Dynamic performance is compassionate to change in system parameters which need more effort for planning a significant controller for CSTR. The reactor temperature changes in either direction from the defined reference value. It is important to note that the intensity of chemical actions inside the CSTR is dependent on the various levels of temperature, and deviation from reference values may cause degradation of biomass quality. Design and implementation of an appropriate adaptive controller for such a nonlinear system are essential. In this paper, a conventional Proportional Integral Derivative (PID) controller is designed. The conventional techniques to deal with constraints suffer severe limitations like it has fixed controller parameters. Hence, A novel method is applied for computing the PID controller parameters using a swarm algorithm that overcomes the conventional controller's limitation. In the proposed technique, PID parameters are tuned by Particle Swarm Optimization (PSO). It is not easy to choose the suitable objective function to design a PID controller using PSO to get an optimal response. In this article, a multi-objective function is proposed for PSO based controller design of CSTR.     

大渡河流域梯级水电站中长期优化调度研究

瀑布沟电站已经正式开始蓄水,采用先进算法对其进行优化调度研究非常必要。针对粒子群算法存在早熟收敛现象和后期振荡现象,给出一种动态改变惯性权的自适应粒子群算法。该算法原理简单,易编程实现,占用计算机内存少,能以较快的速度收敛到全局最优解,从而为梯级水电站中长期优化调度问题提供了一种有效的解决办法。     

SSABA: Search Step Adjustment Based Algorithm

Finding the suitable solution to optimization problems is a fundamental challenge in various sciences. Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new stochastic optimization algorithm called Search Step Adjustment Based Algorithm (SSABA) is presented to provide quasi-optimal solutions to various optimization problems. In the initial iterations of the algorithm, the step index is set to the highest value for a comprehensive search of the search space. Then, with increasing repetitions in order to focus the search of the algorithm in achieving the optimal solution closer to the global optimal, the step index is reduced to reach the minimum value at the end of the algorithm implementation. SSABA is mathematically modeled and its performance in optimization is evaluated on twenty-three different standard objective functions of unimodal and multimodal types. The results of optimization of unimodal functions show that the proposed algorithm SSABA has high exploitation power and the results of optimization of multimodal functions show the appropriate exploration power of the proposed algorithm. In addition, the performance of the proposed SSABA is compared with the performance of eight well-known algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Teaching-Learning Based Optimization (TLBO), Gravitational Search Algorithm (GSA), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA). The simulation results show that the proposed SSABA is better and more competitive than the eight compared algorithms with better performance.     

An approach for the reliability based design optimization of laminated composites

This article aims at optimizing laminated composite plates taking into account uncertainties in the structural dimensions. As laminated composites require a global optimization tool, the Particle Swarm Optimization (PSO) method is employed. A new Reliability Based Design Optimization (RBDO) methodology based on safety factors is presented and coupled with PSO. Such safety factors are derived from the Karush–Kuhn–Tucker optimality conditions of the reliability index approach and eliminate the need for reliability analysis in RBDO. The plate weight minimization is the objective function of the optimization process. The results show that the coupling of the evolutionary algorithm with the safety-factor method proposed in this article successfully performs the RBDO of laminated composite structures.     

Design of multi-objective damping controller for gate-controlled series capacitor

This paper proposes an optimization procedure based on eigenvalues to carry out the stabilization function of the Gate-Controlled Series Capacitor (GCSC) in a power system. It is aimed to provide a reliable damping framework by means of a GCSC based multi-objective damping controller. The proposed method employs Particle Swarm Optimization (PSO) to search for optimal parameter settings of a widely used multi-objective lead-lag damping controller. The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or lightly damped modes are scheduled to effectively shift to some prescribed stability zones in the s-plane. The effectiveness of the suggested approach in damping local and interarea oscillations modes in a multi-machine power system, over a wide range of loading conditions, is confirmed through eigenvalue analysis and time simulation.     

Grey wolf optimizer and hybrid PSO-GWO for reliability optimization and redundancy allocation problem

Analysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics. In this study, a recently developed metaheuristic optimization algorithm called hybrid PSO-GWO (HPSGWO) to solve the reliability-redundancy optimization problem has been proposed. The HPSGWO fuses the Particle Swarm Optimization's (PSO) exploitation ability with the grey wolf optimizer's (GWO) exploration ability. The comparison of results with prior best results of PSO and GWO for the four benchmarks of reliability redundancy allocation problem demonstrates the HPSGWO as a productive enhancement strategy since it got promising answers than other metaheuristic algorithms.     

IC-PSO算法的收敛性分析及应用研究

针对标准PSO算法后期迭代搜索效率不高,容易陷入局部最优的问题,提出将免疫克隆(IC)原理引入PSO算法中,把抗体视为粒子,根据亲和度的高低进行粒子克隆选择、克隆抑制和高频变异,提高了种群的多样性和全局搜索的能力.并将其应用于40Gh/s的传输系统中进行了DOP优化补偿实验,算法补偿所需时间约为71 ms.通过对比补偿前后的信号眼图可以发现,PMD补偿后,信号眼图张开度有明显改善,证明了算法的有效性.