Harmonic Suppression Method Based on Immune Particle Swarm Optimization Algorithm in Micro-Grid

Distributed generation has attracted great attention in recent years, thanks to the progress in new-generation technologies and advanced power electronics. And micro-grid can make full use of distributed generation, so it has been widespread concern. On the other hand due to the extensive use of power electronic devices and many of the loads within micro-grid are nonlinear in nature, Micro-grid generate a large number of harmonics, so harmonics pollution needs to be addressed. Usually we use passive filter to filter out harmonic, in this paper, we propose a new method to optimize the filter parameters, so passive filter can filter out harmonic better. This method utilizes immune particle swarm optimization algorithm to optimize filter parameters. It can be shown from the simulation results that the proposed method is effective for micro-grid voltage harmonics compensation.

An estimation method for direct maintenance cost of aircraft components based on particle swarm optimization with immunity algorithm

A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented. Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Application of Particle Swarm Algorithm in the Optimal Allocation of Regional Water Resources Based on Immune Evolutionary Algorithm

The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic,social and environmental benefits of regional water consumption.In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources,multiple users and multiple objectives like"curse of dimensionality"or sinking into local optimum,this paper proposes a particle swarm optimization(PSO)algorithm based on immune evolutionary algorithm(IEA).This algorithm introduces immunology principle into particle swarm algorithm.Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population.Also,the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum.After applying this model to the allocation of water resources in Zhoukou,we obtain the scheme for optimization allocation of water resources in the planning level years,i.e.2015and 2025 under the guarantee rate of 50%.The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable.Thus it ofers a new idea for solving the issue of optimal allocation of water resources.

基于人工免疫-改进粒子群优化算法的机械臂轨迹规划研究

焊接机器人运动轨迹复杂、控制精度要求高。提出了一种满足多目标约束的轨迹规划方法。针对机器人轨迹平滑性要求,以5次非均匀有理B样条(Non-Uniform Rational B-Splines,NURBS)曲线为基础,对笛卡儿空间路径点进行参数化表达;根据工业机器人路径约束及工况需求,选取时间、能耗、跃度3个运动学指标作为目标优化函数,采用人工免疫双态粒子群进行轨迹优化;为了平衡粒子“探索”与“利用”,增加双模态模型,引入人工免疫系统,提升了粒子多样性与后期收敛能力;根据Pareto解集得到满足约束的焊接机器人各关节最优轨迹,通过Matlab仿真证明了方法的有效性;最后,针对空间相贯曲线焊缝进行了焊接试验。结果显示,规划的轨迹符合实际工程需求。

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。

免疫粒子群算法在修正高斯模型下的源强反演

为提高危险气体泄漏溯源定位的科学性和实效性,确定危险气体泄漏位置和强度是事故应急响应的关键。首先,根据质量守恒定律,分析、改进近似高斯分布的气体羽流扩散幅度,修正高斯烟羽模型;然后,基于免疫浓度筛选机制作为主策略的免疫算法(IA),通过与粒子群算法(PSO)耦合,将混合免疫粒子群(PSO-IA)算法应用到源强反演中;最后,验证PSO-IA算法溯源定位效果。结果表明:与模式搜索法(PS)、遗传算法(GA)、PSO相比,修正高斯烟羽模型预测值误差均下降2%左右;混合PSO-IA算法相较PSO算法反演源强效果有明显提升,其算法定位误差为1.3 m,求解源强误差为0.8%,单次计算时间小于1 s,能实现快速、准确定位并估算源强度。

一种基于用户通勤行为的家庭能量管理优化策略

随着电动汽车快速发展,V2G技术可大幅降低家庭能量管理系统中用户的用能成本,但V2G会影响用户出行。针对此问题提出了一种基于用户通勤行为的家庭能量管理系统优化策略,通过极大似然估计和蒙特卡罗模拟构建用户出行模型,其次将杂交粒子群与混沌算法、免疫算法相融合,利用多重混沌免疫杂交粒子群算法(MCIHPSO)对目标函数进行求解,最后,通过仿真及实验验证了本文所提控制策略显著降低V2G功能对用户出勤的影响。

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Hybrid anti-prematuration optimization algorithm

Heuristic optimization methods provide a robust and efficient approach to solving complex optimization problems.This paper presents a hybrid optimization technique combining two heuristic optimization methods,artificial immune system（AIS） and particle swarm optimization（PSO）,together in searching for the global optima of nonlinear functions.The proposed algorithm,namely hybrid anti-prematuration optimization method,contains four significant operators,i.e.swarm operator,cloning operator,suppression operator,and receptor editing operator.The swarm operator is inspired by the particle swarm intelligence,and the clone operator,suppression operator,and receptor editing operator are gleaned by the artificial immune system.The simulation results of three representative nonlinear test functions demonstrate the superiority of the hybrid optimization algorithm over the conventional methods with regard to both the solution quality and convergence rate.It is also employed to cope with a real-world optimization problem.

基于免疫粒子群混合算法优化BP网络的矿压预测方法

为攻克综采工作面顶板矿压显现规律预测预报的难题,构建一种基于免疫粒子群混合算法优化BP神经网络的矿压预测模型(IA-PSO-BP),针对BP神经网络收敛速度慢和易陷入局部最优的问题,采用免疫粒子群混合算法优化BP神经网络,并选取11种矿压主要影响因素作为模型基础数据,对工作面来压强度和来压步距进行预测。结果表明:IA-PSO-BP网络模型的收敛速度较BP网络模型和PSO-BP网络模型分别提高8倍和2倍,IA-PSO-BP网络模型的预测值与实测值基本吻合,预测结果的相对误差分别约为BP网络模型和PSO-BP模型的1/5和1/3。基于IA-PSO-BP的工作面矿压预测方法具有较快的收敛速度和较高的准确率,实现了工作面初次来压强度、周期来压强度、初次来压步距和周期来压步距距预测,为煤矿井下工作面矿压预测提供了一种新的技术途径。

基于IMPSO的双韦伯燃烧参数标定及预测

针对柴油机放热规律的拟合,提出了基于免疫粒子群优化(IMPSO)算法的双韦伯方程标定方法.对25%负荷和100%负荷的放热规律分别进行了标定,对双韦伯方程采用改进算法和免疫算法进行多解问题优化,优化结果表明:量纲为1的决定系数R2大于0.998,最优解稳定性可以达到0.700以上;对其他工况和机型放热规律进行标定,拟合和试验结果吻合,具有良好的泛化性,满足柴油机多工况的放热规律拟合要求;选择转速、循环喷油量、进气压力和进气温度作为输入,建立了多层前馈(BP)神经网络双韦伯方程预测模型,各参数R2均大于0.950,表明预测值与校准值吻合,验证了神经网络预测建模方法的可行性.

基于改进免疫算法的工业通信网络控制器故障位置检测

以单一智能挖掘算法为主的控制器故障位置检测很容易陷入局部最优,导致检测难度增加,为此该文提出基于改进免疫算法的工业通信网络控制器故障位置检测方法。设计工业通信网络控制器故障告警关系二分图与关系边约束,建立控制器故障关联函数。采用小波包提取故障关联函数输出的高频数据特征,将其作为免疫系统的原始种群。通过改进免疫算法得出最优抗体,最优抗体所在位置即为故障位置,得到故障位置检测结果。实验结果表明,该方法可以有效缩短控制器故障位置检测耗时以及误检率,提升检测效果。

基于改进混沌粒子群优化算法的永磁同步电机参数辨识

针对永磁同步电机(PMSM)在参数辨识过程中由于粒子容易早熟和陷入局部最优而导致辨识精度不高的问题,提出了一种改进混沌粒子群优化(ICPSO)算法,并将其应用在PMSM多参数辨识中。该算法通过对混沌算法和粒子群优化(PSO)算法结合并优化,且在算法中融入精英免疫原理,处于中间的粒子进行免疫升级,此举不仅扩大了粒子在种群中的搜索范围,而且在一定程度上克服了粒子早熟、不易跳出局部最优的问题。该算法对标准测试函数进行试验,且与PSO算法和混沌粒子群优化(CPSO)算法在参数辨识中的效果相比较,得出定子电阻、dq轴电感和转子磁链电磁参数,从而证明该算法的有效性。

基于多源数据的充电站三阶段选址优化决策

为满足日益增长的充电需求,提出一种计及选址影响因素和建设成本的充电站三阶段规划方案,实现了选址评估-预选址-站点增扩的有机结合。第一阶段,通过多源数据对影响因素展开分析,利用层次分析法建立选址评价体系。第二阶段,从用户和企业的角度出发,建立以成本最小为目标的选址优化模型,同时,提出了一种改进的免疫粒子群优化(immune particle swarm optimization,IPSO)算法,用以实现解在空间范围内的自适应搜索。第三阶段,考虑到充电站的增扩问题,设计了一种利用Voronoi图确定新增站点的方法。最后,以某市为研究对象,通过仿真验证了所提模型的有效性与合理性,为充电站的科学选址提供了必要的决策参考。

基于免疫粒子群的机器人自抗扰控制器参数整定方法

针对利用经验试凑法或其他优化算法整定机器人自抗扰控制器参数,存在整定过程复杂、整定结果不是全局最优等缺陷,提出一种基于免疫粒子群融合算法的机器人自抗扰控制器参数整定方法。该方法将免疫算法的免疫信息处理机制引入粒子群算法结构中,解决了免疫算法优化过程繁杂冗长以及粒子群算法过早陷入局部最优的问题,实现了自抗扰控制器参数整定,快速找到全局最优解。MATLAB仿真结果表明:该方法提高了自抗扰控制器的响应速度和稳定性,适用于机器人自抗扰控制器参数的整定。

基于IA-BP智能算法的初始地应力场反演研究

初始地应力场是地下工程设计与施工的重要依据,在实际工程中难以精准测得,为了能较准确地获得初始地应力场的分布规律,提出将免疫算法与BP神经网络相结合(IA-BP)的算法对初始地应力场进行反演研究。免疫算法优化BP神经网络就是将BP神经网络的连接权值和阈值作为免疫算法中的抗体进行编码。该混合算法既可以利用免疫算法全局寻优的特点快速搜索到全局最优解或次优解附近,又可以采用BP算法避免在最优解和次优解附近发生震荡,对其进行局部优化,从而达到快速收敛全局最优解的目的。通过COMSOL分别构建平面边坡模型及三维立体模型,对其进行正分析计算,将计算的结果作为“实测值”,对地应力进行反演分析,并将IA-BP算法反演的结果与PSO-BP算法及多元线性回归算法的反演结果进行对比。结果表明:二维边坡模型下,IA-BP算法反演结果误差更小。三维模型下,IA-BP算法所得实测值与反演值之间的相对误差的绝对值为0%~10.64%(平均为3.39%),PSO-BP算法所得实测值与反演值之间相对误差的绝对值为0%~48.39%(平均为6.93%),多元线性回归算法所得实测值与反演值之间相对误差的绝对值为0.55%~121.95%(平均为21.87%),通过对比可知,IA-BP算法整体反演结果精度最高。无论是平面模型还是三维模型,利用IA-BP算法反演出的结果与其他两种算法反演的结果相比,误差更小。将IA-BP智能算法运用到地应力场的反演研究中,可以为地下工程的建设提供依据。

基于双层规划的配送中心选址及路径优化方法研究

随着物流系统网络的不断扩大,其选址和配送问题也逐渐凸显。为了解决这一问题,文中建立了选址配送双层规划模型,上层为多配送中心选址模型,下层为车辆路径模型。在对该模型中使用免疫算法进行选址;在路径规划方面为降低算法的运行时间,使用贪婪算法得到初始解,然后利用粒子群算法进行路径规划,求解流程为通过上层选址方案分配客户,然后得到下层配送方案,根据选址成本和配送成本最终得到相应的选址方案和车辆调度方案。结果表明,该模型得到的结果对物流选址和车辆配送具有一定的参考价值。

进化多目标优化算法研究

进化多目标优化主要研究如何利用进化计算方法求解多目标优化问题,已经成为进化计算领域的研究热点之一.在简要总结2003年以前的主要算法后,着重对进化多目标优化的最新进展进行了详细讨论.归纳出当前多目标优化的研究趋势,一方面,粒子群优化、人工免疫系统、分布估计算法等越来越多的进化范例被引入多目标优化领域,一些新颖的受自然系统启发的多目标优化算法相继提出;另一方面,为了更有效的求解高维多目标优化问题,一些区别于传统Pareto占优的新型占优机制相继涌现;同时,对多目标优化问题本身性质的研究也在逐步深入.对公认的代表性算法进行了实验对比.最后,对进化多目标优化的进一步发展提出了自己的看法.

基于免疫粒子群算法的非合作博弈Nash均衡问题求解

针对N人非合作博弈Nash均衡求解问题,将免疫算法中抗体浓度抑制机制和免疫记忆功能引入基本粒子群算法,提出了一种求解博弈问题Nash均衡的免疫粒子群算法。该算法通过抗体浓度抑制机制和免疫记忆功能来保持种群的多样性,不仅保持了粒子群算法简单、易于实现的特点,而且增强了粒子群算法的全局寻优能力,加快了算法的速度。实验表明,提出的算法具有较好的性能,优于免疫算法和基本粒子群算法。