Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

Optimal Power Flow Solution Using Particle Swarm Optimization Technique with Global-Local Best Parameters

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

A New Class of Hybrid Particle Swarm Optimization Algorithm

A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.

Enhancement in Channel Equalization Using Particle Swarm Optimization Techniques

This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.

基于视觉传达技术的人机交互界面优化设计研究

为了提升人机交互界面的布局合理性,增强用户满意度,研究基于视觉传达技术的人机交互界面优化设计方法。利用三维空间重建方法,从人机交互界面视觉传达图像的中心点出发,依据视觉传达图像平行曲线的中心以及局部脊线,三维重建视觉传达图像。计算视觉传达图像三维重建结果中各视觉感知元素的视觉传达指数。以人机交互界面的视觉传达指数最大为目标,构建人机交互界面优化模型。选取粒子群算法求解人机交互界面优化模型,引入动态惯性权重改进粒子群算法,输出人机交互界面的优化设计方案。实验结果表明,该方法能够实现人机交互界面中视觉感知元素的高效布局,人机交互界面优化设计后的峰值信噪比高于30 dB。

基于径向基函数与Sigmoid函数的改进粒子群算法

针对粒子群算法求解精度低和收敛速度慢等问题,提出一种基于径向基函数与Sigmoid函数的粒子群算法。通过引入径向基函数和Sigmoid函数,分别对惯性权重和位置更新公式进行改进,从而提高算法的全局搜索能力和搜索效率;最后利用6个基准测试函数对算法的性能进行实验验证和分析。实验结果表明,改进后的算法能够收敛到全局最优值,并且在收敛速度和求解精度上均有较大提高。

基于改进粒子群算法优化的染色木材颜色检测算法研究

为提高染色木材颜色的检测精度和速度,对樟子松木材单板进行染色,选取染色单板的光谱反射率作为输入,以极限学习机模型为基础构建预测模型,对染色单板的色度参数L^(*)、a^(*)、b^(*)进行预测,运用粒子群算法对ELM权值和阈值进行寻优,并引入非线性惯性权重和新的位置与速度更新策略改进粒子群算法,以消除其易陷入局部最优的缺点。此外,以L^(*)、a^(*)、b^(*)平均绝对误差为评价指标,与基础ELM模型及其他模型作对比,发现优化后的模型平均绝对误差为0.16,测色效果相较于基础ELM的0.68、麻雀算法优化的ELM的0.37等具有明显优势,这对于提高木材染色生产效率具有重要意义。

一种非线性动态权重的粒子群寻优改进算法

在传统的线性递减惯性权重粒子群优化算法(Linearly Decreasing Inertia Weight Particle Swarm Optimization,LDW-PSO)中,惯性权重通过一个固定的线性递减方式进行调整。这种方法在适应具体问题和反映算法当前状态方面存在局限性。为了克服这些限制,在线性递减权重的基础上引入了幂律分布函数,提出了一种新的自适应权重计算方法。该方法使得惯性权重能够根据迭代次数的增加,按照幂律函数的非线性规律逐渐减小,从而在算法的早期阶段增强全局搜索能力,在后期阶段更侧重于局部搜索。通过这种灵活的权重调整,改进方法能够有效提升粒子群优化算法的性能。为了验证所提出方法的有效性,采用4种基准测试函数进行性能分析。实验结果表明,与传统的线性递减权重方法和标准粒子群优化算法相比,改进后的算法展现了更优的搜索能力和更好的收敛性能。

基于离散粒子群算法的管道保温结构优化研究

针对目前管道保温结构优化算法不稳定、结果优化程度不高的问题,建立以经济效益为目标函数,以满足国家散热损失标准等条件为约束函数的离散型数学模型。以BPSO算法为基础改变其位置更新规则,防止种群进化失效;采用自适应权重增加粒子的全局和局部搜索能力;充分利用模拟退火算法的思想避免出现早熟现象。应用改进的算法分别对普通蒸汽管道和核电站的蒸汽管道进行系统仿真实验。结果表明,该算法能够在满足国家散热损失标准等条件下取得最优解,可以为管道保温结构提供合理的优化方案。

基于巴特沃斯幅频特性的自适应粒子群算法

针对传统粒子群算法存在求解精度低和易陷入局部最优等问题,提出一种基于巴特沃斯幅频特性的自适应粒子群算法(Butterworth amplitude-frequency characteristics based adaptive particle swarm optimization algorithm,BAC-PSO).一方面,借助巴特沃斯幅频特性设计一种惯性权重非线性递减策略,均衡算法中粒子的局部与全局搜索能力;另一方面,通过S型函数的粒子群优化策略和Sigmoid函数改进位置更新方法,进一步提升算法的求解精度.以5个经典的测试函数为基准,将BAC-PSO算法与5种经典粒子群算法的性能进行对比,并将其应用到求解压力容器模型的设计问题中.实验结果表明,相较于其他经典粒子群算法,BAC-PSO算法的求解精度更高,收敛速度更快,稳定性更好.

免疫粒子群算法的测试数据生成

为有效改善粒子群算法进化后期收敛速度慢,克服易陷入局部极值的缺陷,提出一种自适应免疫粒子群算法并在面向路径的测试数据生成中得到应用。本文提出自适应的惯性权重的调整方法和学习因子的调节策略,加快算法的搜索速率;引入免疫算法中的免疫算子,提出抗体的浓度调节机制,使得粒子群的多样性更加丰富,提升算法的寻优能力;通过免疫选择操作,避免算法的早熟收敛;以分支函数叠加法构造适应度函数。实验结果表明,该算法避免了粒子群算法早熟收敛现象的发生,有效地提高了测试数据自动生成的效率。

改进粒子群算法在柔性车间调度问题的研究

为了减少柔性作业车间调度问题加工时间,通过前景理论构建柔性作业车间加工满意度数学模型,以降低加工时间为目标,设计了一种惯性权重幂函数调节的改进粒子群算法(PPSO),该算法惯性权重能够幂函数自适应调节,学习因子能够发生相应的递减或递增变化平衡算法的全局探索和局部开发能力。选取BRdata与Kacem经典算例,通过与人工免疫算法(AIA)和整合模拟退火算法(ISA)的进行仿真比较,PPSO加工时间较短;在工件数J=25、可用机器数M=16、迭代次数600情况下比较BRdata算例,PPSO加工时间相较AIA、ISA算法分别缩短了21.61%和4.32%,验证结果表明PPSO算法在柔性车间调度问题中降低产品加工时间的有效性。

非线性递减权值PSO优化下的LQR轨迹跟踪研究

针对二次线性调节器(LQR)权重矩阵选取困难导致的自动驾驶车辆控制精度低、系统适应度欠佳等问题,设计了一种非线性递减权值粒子群算法(NLDW-PSO)。基于二自由度车辆动力学模型,构建了横向跟踪误差模型,设计了前馈控制消除了LQR稳态误差;并设计以横向偏差、航向偏差和前轮转向角为评价函数,将系统输出误差状态量反馈至NLDW-PSO算法,所设计的非线性递减惯性权重因子通过提升粒子群体寻优性能,从而自适应调整LQR权重系数更新策略,形成闭环优化控制,最终求解得到系统目标函数极值。将所设计控制器的跟踪效果进行了对比,Carsim/Smulink联合仿真结果表明所提出NLDW-PSO优化LQR算法的跟踪控制效果最优,横向距离偏差最大值为0.076 m,横向距离偏差均值相较于固定权重系数LQR降低了69.74%,显著提高了车辆跟踪控制精度和自适应能力,且对速度变化具有较强鲁棒性。

基于改进粒子群算法的湿法冶金技术优化控制

分析了湿法冶金技术的关键工艺,构建了优化控制模型,并利用自适应惯性权重和模拟退火算子对粒子群算法进行改进,对湿法冶金技术进行优化控制。仿真试验结果显示:在A风力发电场优化数据集中测试中,AIW-SAO-PSO算法迭代225次时趋于稳定,适应度值约为0.165,且迭代100次时,算法的均方根误差、平均绝对误差、相对标准偏差分别为0.0080,0.0045和0.971%;在湿法冶金技术优化控制模型的寻优求解中,得到的综合效益值为1.9×10^(5)元/h,与目标期待值的绝对误差约为0.1×10^(4)元/h。实现了湿法冶金技术的优化控制,并为同类型优化控制提供理论支持。

基于改进PSO-SVM的薄壁件铆接质量检测

针对传统铆接几何公差质量检测极易造成错检、漏检等问题,提出了基于改进PSO-SVM的铆接质量检测方法。采用惯性权重自适应调整的策略,并选择合适的学习因子,有效提高了检测准确性;针对小样本提出最小二乘SVM算法,提高计算速度获得最优解;利用改进PSO优化最小二乘SVM的惩罚因子参数值和核函数参数值。并以制孔和铆接后的6061铝合金板模拟飞机薄壁件铆接样本,使用搭配远心镜头的CCD相机采集图像并建立数据集,验证了方法的有效性。

基于粒子群算法的电网资源最优匹配方法

为了合理匹配电网资源,维护电网的安全性、稳定性以及高效性,提出基于粒子群算法的电网资源最优匹配方法。因标准粒子群算法的性能会受到学习参数和惯性权重参数的牵制,容易进入局部最优值的僵局,故优化标准粒子群算法。以网损最低、电压偏差最低、无功补偿容量最小三项作为电网资源最优匹配的目标函数,约束条件为:潮流平衡约束、控制变量约束和状态变量约束,最后通过优化后的粒子群算法求解出目标函数。实验结果可知,该方法可以有效求解三项目标函数,使其对电网资源的匹配结果达到最优。

基于有限元分析与智能粒子群优化算法优化坡面框架支护梁截面设计

将改进自适应惯性权重智能粒子群优化算法(PSO)与外点罚函数结合,以跨长为6 m承受均布荷载的钢筋混凝土坡面支护框架梁为例进行研究。利用FLAC-3D有限元模拟均布荷载下梁的受力情况。研究发现梁单元在跨中弯矩达到阈值100 kN·m时双筋梁造价相对单筋梁低;且保持外力不变时增大混凝土强度使全梁造价升高,而增大钢筋强度使造价下降;箍筋应在满足规范的要求下减小间距;文章还根据计算结果给出相应解释。