A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

Support vector machine forecasting method improved by chaotic particle swarm optimization and its application

By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively,a new hybrid algorithm named CDEPSO is proposed in this work,which combines particle swarm optimization (PSO) with differential evolution (DE) and a new chaotic local search.In the CDEPSO algorithm,DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO,while the best solution in the PSO is performed by chaotic local search.To investigate the performance of CDEPSO,four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO (a hybrid algorithm which only combines PSO with chaotic local search).And,compared with the other six improved meta-heuristics,CDEPSO also exhibits more robust performance.In addition,a new performance was proposed to more fairly compare CDEPSO with the same six improved meta-heuristics,and CDEPSO algorithm is the best in solving these problems.

Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy

To deal with the problems of premature convergence and tending to jump into the local optimum in the traditional particle swarm optimization, a novel improved particle swarm optimization algorithm was proposed. The self-adaptive inertia weight factor was used to accelerate the converging speed, and chaotic sequences were used to tune the acceleration coefficients for the balance between exploration and exploitation. The performance of the proposed algorithm was tested on four classical multi-objective optimization functions by comparing with the non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results verified the effectiveness of the algorithm, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.

Design of Radial Basis Function Network Using Adaptive Particle Swarm Optimization and Orthogonal Least Squares

This paper presents a two-level learning method for designing an optimal Radial Basis Function Network (RBFN) using Adaptive Velocity Update Relaxation Particle Swarm Optimization algorithm (AVURPSO) and Orthogonal Least Squares algorithm (OLS) called as OLS-AVURPSO method. The novelty is to develop an AVURPSO algorithm to form the hybrid OLS-AVURPSO method for designing an optimal RBFN. The proposed method at the upper level finds the global optimum of the spread factor parameter using AVURPSO while at the lower level automatically constructs the RBFN using OLS algorithm. Simulation results confirm that the RBFN is superior to Multilayered Perceptron Network (MLPN) in terms of network size and computing time. To demonstrate the effectiveness of proposed OLS-AVURPSO in the design of RBFN, the Mackey-Glass Chaotic Time-Series as an example is modeled by both MLPN and RBFN.

Dynamic Self-Adaptive Double Population Particle Swarm Optimization Algorithm Based on Lorenz Equation

In order to improve some shortcomings of the standard particle swarm optimization algorithm, such as premature convergence and slow local search speed, a double population particle swarm optimization algorithm based on Lorenz equation and dynamic self-adaptive strategy is proposed. Chaotic sequences produced by Lorenz equation are used to tune the acceleration coefficients for the balance between exploration and exploitation, the dynamic self-adaptive inertia weight factor is used to accelerate the converging speed, and the double population purposes to enhance convergence accuracy. The experiment was carried out with four multi-objective test functions compared with two classical multi-objective algorithms, non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm. The results show that the proposed algorithm has excellent performance with faster convergence rate and strong ability to jump out of local optimum, could use to solve many optimization problems.

基于混沌粒子群改进支持向量机对露天矿边坡稳定性的分类预测

为了简便有效地评估边坡稳定性状态,针对目前传统机器学习的算法选择与超参数优化等难题,提出了基于混沌粒子群优化算法的4种机器学习模型,并对其预测性能进行了对比。建立了包含221组露天矿边坡稳定性案例的数据库,其中80%的数据用于训练,20%的数据用于模型测试。4种模型预测结果及工程实例验证结果表明,基于混沌粒子群改进支持向量机模型的预测效果上总体优于其他3种机器学习模型,预测准确率88%,能够有效预测边坡稳定性,可为露天矿边坡安全提供可靠的预测结果。

基于多变量灰色系统的乏信息堤防变形短期预测模型

依据信息模糊和不确定状态下乏信息数据处理理论,提出了一种改进多变量灰色系统的乏信息堤防短期预测模型;引入多变量灰色模型对多测点的沉降变形序列进行拟合,结合混沌粒子群优化算法和分数阶微积分理论,实现了在乏信息条件下对堤防多测点变形的短期预测;由对比结果可知,研究提出的模型可行且有效,填补了堤防乏信息处理模型的空白。

基于混沌多目标粒子群算法的综合能源调度

目的针对当前综合能源系统中资源协同优化效率不足、微网运行经济性和环保性差的问题,提出了一种计及风电储能及不稳定因素的微网优化调度方法。方法该方法在微网负荷侧需求响应对新能源消纳影响的基础上,以消纳新能源和削峰填谷为目的,提出了优化负荷曲线的方案;然后,考虑微网调度侧风电出力的不稳定性以及微网内部设备的耦合,进行优化调度以降低微网运行成本、减少环境惩罚费用并提高风电消纳平稳性;最后,采用混沌多目标粒子群算法对优化问题进行求解,并在风电不稳定度占比0%、5%、10%和15%时进行了算例仿真分析。结果当风电不稳定度为10%和加入风电储能,系统运行成本和环境治理费用最少,比方案1和无风电储能少6919.4元,风电平稳量也提高38 kWh。在电热冷网中,负荷侧加入需求响应后,系统得到稳定运行和能源合理利用,可以很好地满足负荷侧用能需求。从算法对比中,混沌多目标粒子群算法加入自适应权重和变异率后,具有较强的全局搜索能力和更好的准确性。结论该方法通过合理设置风电不稳定度能够有效降低运行成本和环境惩罚费用,提高风电稳定性,其次,负荷侧的需求响应可以一定程度地削峰填谷和消纳新能源。

基于CPSO-Elman神经网络矿井下可见光定位

针对传统矿井下定位方法精度偏低问题,提出一种混沌粒子群优化(CPSO)Elman神经网络矿井下可见光定位系统。由于Elman神经网络在初始化时存在参数设置的随机性导致预测精度不高,采用CPSO算法优化Elman神经网络,选取适合的各层的初始权值和阈值,用于提高神经网络拓扑的稳定性。仿真结果表明:在3.6 m×3.6 m×3.6 m的环境里,本文所提的算法的平均定位误差达到3.70 cm,最大定位误差为26.54 cm,在实验阶段的平均定位误差为5.91 cm,最大定位误差为36.95 cm,能够满足煤矿井下定位需求。

ACCQPSO:一种改进的量子粒子群优化算法及其应用

针对量子粒子群优化算法前期易陷入局部极值点、后期寻优精度不高等问题,文章提出一种自适应交叉算子的混沌量子粒子群优化算法,并将其应用于BP神经网络超参数寻优。首先,利用Logistics映射初始种群为混沌序列进行最优解搜索,增强初始种群的随机性与遍历性,提高算法寻优能力;然后,通过纵向交叉操作进行种群中个体的信息交换,并引入自适应交叉概率公式,增加种群多样性,提高算法的寻优精度;最后,在实验中,一方面,选取8个函数在高低两个维度进行验证,同时进行Wilcoxon秩和检验分析以及消融实验,验证该算法相较其他算法的有效性;另一方面,通过算法优化BP神经网络应用到网络安全态势预测任务中,实验结果表明该算法收敛速度相较于对比算法有大幅度提升。

基于寻优算法的双馈风机变流器动态运行控制参数辨识

针对运行过程中双馈风机变流器控制参数难以获取的问题,提出了一种基于自适应混沌粒子群算法的转子侧变流器参数辨识方法。首先,基于机组实际运行下可量测电气量时间序列,建立双馈风机变流器控制系统离散化数学模型;然后,根据不同观测电气量下参数的轨迹灵敏度,对辨识难易程度进行分析;最后,利用自适应混沌粒子群算法对变流器PI控制参数进行辨识。仿真实验结果验证了所提出辨识方法的准确性与可行性。

基于AMCPSO优化Kriging插值的温度补偿方法研究

为了降低温度变化对转换力传感器测量精度的影响,提出一种自适应变异混沌粒子群算法(AMCPSO)优化Kriging插值的温度补偿算法(AMCPSO-Kriging)。研发转换力传感器,分析温度对传感器输出的影响,建立温度补偿标定实验平台,通过标定实验获得建立温度补偿模型所需要的样本集,采用数据稀疏化方法对样本数据进行优化。通过Kriging插值构建了温度补偿模型,利用AMCPSO算法以交叉验证方式下模型预测产生的均方根误差和作为适应度函数,对Kriging插值中的范围参数θ和平滑度参数pk进行寻优求解,得到性能最佳的温度补偿模型。基于AMCPSO-Kriging温度补偿模型对转换力传感器的测量效果进行实验验证,与标准力传感器进行对比。实验结果表明:对样本数据进行稀疏化处理,算法平均运行时间从1076 s减少到6 s,提高了温度补偿算法的运行效率。在−20~70℃温度范围内,经过AMCPSO算法优化的Kriging模型有效提高了转换力传感器的测量精度,相比于未经AMCPSO算法优化的Kriging插值,转换力传感器测量的平均满量程误差从1.2%FS降低到0.6%FS。通过现场实验验证温度补偿的效果,转换力传感器测量的绝对误差在70 N以内,最大满量程误差为2.3%FS。所提出的温度补偿方法有效消除了温度对传感器测量精度的影响,满足铁路工况使用要求,对转换力传感器在铁路上实际运用具有重要价值。

不平衡电压下双笼转子无刷双馈发电机的多目标控制

该文以双笼转子无刷双馈感应发电机为主要研究对象。为了解决不平衡电网电压带来的功率绕组(power winding,PW)定子绕组功率脉动和电流不平衡的问题,设计了简化矢量控制策略,对PW侧谐波功率和负序电流进行抑制。此外,通过建立控制绕组(control winding,CW)电流统一表达式,对各个控制目标之间的关系进行了数学推导和理论分析。在此基础上,建立了PW电流不平衡度约束条件下,针对PW有功功率脉动幅值和无功功率脉动幅值的多目标控制数学模型。并采用粒子群算法进行求解,对CW统一电流表达式中的系数进行优化,计算CW参考电流值从而实现多目标控制。最后在双笼转子无刷双馈感应发电机实验平台上,对所提出的控制策略的有效性和理论推导的正确性进行了验证。

基于改进粒子群算法的工业机器人轨迹规划

针对传统工业机器人轨迹规划工作效率低的问题,提出一种多约束条件下的改进粒子群轨迹优化算法。采用4-3-4分段多项式对工业机器人的路径点进行轨迹拟合,运用改进粒子群算法对工业机器人进行时间最优轨迹规划。该方法引入基于混沌映射的惯性权重更新策略、终端弹性机制和交叉机制,使粒子在迭代后期跳出局部最优,平衡了粒子的全局探索能力和局部开发能力。实验结果表明:采用改进粒子群算法对机器人进行轨迹优化,能够使目标函数快速收敛,并获得满足各关节位置、速度和加速度约束的全局最优时间轨迹。与传统方法相比,该方法使得机器人在约束条件下工作时间最短、运行速度最快,在保证运行平稳的前提下较为明显地提升了机器人的工作效率。

考虑配电网脆弱性的储能双层优化配置模型

针对大规模分布式电源并网时配电网调控能力不足导致的配网脆弱性差、供需不平衡及运维成本增加等问题,立足储能设备的灵活响应特性,提出1种计及储能配置和运行耦合的双层协同优化模型。上层模型考虑储能系统配置成本、网络运行损耗成本和主网购电成本,探究储能最优配置方案;下层模型以优化电网综合脆弱性为目标感知电网运行的改善潜力,修正上层储能配置方案并制定储能能量管理策略。引入混沌理论及动态惯性权重策略优化算法性能,以改进的混沌粒子群算法求解优化模型。算例分析表明,所提储能配置方案及运行优化策略有效改善了电网运行脆弱性,提高了配电网运行水平。

基于混沌粒子群优化算法的反应釜温度预测控制研究

反应釜作为化工行业核心的生产容器,其温度控制优化在化工生产领域中具有重要作用。针对反应釜温度控制难的问题,提出了一种基于Tent映射的混沌粒子群优化(CPSO)算法优化动态矩阵控制(DMC)-比例积分微分(PID)的反应釜温度预测控制策略。由于DMC很难选取较优的参数,利用Tent映射的CPSO算法提高动态矩阵参数寻优的速度。通过试验,以及与常规PID、DMC-PID控制对比分析,基于Tent映射的CPSO-DMC-PID串级控制对温度控制系统有较好的控制精度和响应速度,可大幅缩小超调量。该控制策略对反应釜温度预测控制研究具有一定的参考意义。

多项式插值改进的乒乓球拾球机械臂运动控制

乒乓球拾球机器人在工作过程中易受到场地、机械结构等因素的制约,不能精准拾取乒乓球。为此,提出多项式插值改进的乒乓球拾球机械臂运动控制。通过乒乓球捡拾机器人工作示意图及机械臂优化原理,优化设计拾球机械臂的结构,利用3-5-3多项插值法构建拾球机械臂轨迹的轨迹函数,并在此基础上利用混沌粒子群算法实现拾球机械臂的轨迹规划。实验结果表明,所提方法能够精确地对拾球机械臂的轨迹实施规划,并且其受随机扰动的影响较小,控制误差平均值为0.01,具有良好的控制效果。