新的全局—局部最优最小值粒子群优化算法

为了提高粒子群优化算法的收敛速度,克服陷入局部最优的缺点,在全局—局部最优粒子群优化算法的基础上,提出了一种新的改进粒子群优化算法——全局—局部最优最小值粒子群优化算法。该算法把惯性权重和学习因子分别通过结合全局和局部最优最小值来进行改写,速度更新公式也做了相应的简化。仿真实验表明该算法在收敛速度和寻优质量上都优于基于LDIW策略改进的粒子群算法和全局—局部最优粒子群算法。

基于改进粒子群算法的资源受限项目进度研究

资源受限的项目进度问题是经典的NP-hard问题,在研究以往求解方法的基础上,应用一种新的群智能算法——粒子群算法,对粒子群优化算法的搜索能力进行改进,结合Gbest模型与Pbest模型的优点,提出使粒子在搜索的前期有较强的全局搜索能力,尽可能多地发现可能全局最优的种子,而在搜索的后期则具有较强的局部搜索能力,用提高算法的收敛速度和精度的复合最优模型粒子群算法对RCPSP问题进行了求解,最后用文献[8]中的算例进行了仿真实验,实验结果验证了此算法的可行性与优越性。

基于合作粒子群算法的PID神经网络非线性控制系统

PID神经元网络(PIDNN)模型为一种新型的神经网络模型,兼有PID与神经网络的共同优点,应用于复杂的控制系统,取得优良控制性能,但其后向传播算法(BP)限制了该模型的应用范围.为实现对非线性多变量系统的有效控制,扩展神经网络的应有范围,本文采用PIDNN神经网络设计了多变量PIDNN神经网络(MPIDNN)控制器,并用本文作者提出的合作粒子群算法(CPSO)取代了传统BP后向传播算法,通过比较MPIDNN_CPSO、MPIDNN_CRPSO、MPIDNN_PSO和MPIDNN_BP4种控制器的控制性能,仿真结果表明,基于CPSO算法的MPIDNN控制器实现了对非线性多变量不对称系统的有效控制.与传统的BP算法相比,CPSO算法提高了控制系统的稳定性、精确性与鲁棒性.

基于改进粒子群优化算法的电力系统有功最优潮流

提出了一种基于改进粒子群优化算法的有功最优潮流模型及求解方法,采用了自适应罚函数法处理最优潮流问题的各种约束条件。通过对IEEE-30节点系统的仿真计算,并且与遗传算法进行比较,验证了提出的模型和方法的有效性。

基于PSO优化LS-SVM算法的网络空间态势预测研究

为了监测网络安全和基于网络空间态势变化表现出高度的非线性、时变性以及不确定性等特征,提出了基于最小二乘支持向量机(LS-SVM)的态势预测方法。研究中考虑到数据的精准程度,利用粒子群算法(PSO)优化LS-SVM算法,并进行了仿真实验。与传统支持向量机和基于遗传优化的支持向量机算法进行比较,可知基于粒子群优化的LS-SVM算法具有更高的精度性和准确性,能够将预测结果误差控制在0.7%以内。

分布式光伏接入的配电网规划综合评价方法

为充分考虑分布式光伏接入对配电网规划评价的影响,基于传统配电网的规划评价体系,提出一种计及光伏输出功率随机性和相关性的有源配电网规划评价方法。首先,为精准刻画光伏出力的随机性与波动性,提出基于改进最优粒子群算法的高斯混合模型,计算多个光伏出力的联合概率密度函数;然后,将潮流方程线性化,推导节点电压和线路潮流线性表达式,分别获取多节点电压和多线路潮流的联合概率分布,并基于此构建考虑光伏接入后的配电网可靠性指标和电压质量指标等。最后,将新构建的电压越限风险指标、电压偏差指标以及潮流断面越限风险指标等新型电网规划评价指标纳入评估体系中,采用层次分析法计算得到组合权重并组合得到最终综合评价结果。仿真结果验证了该评价方法的有效性。

面向最优效率的潜标耦合电能传输系统

将耦合电能传输技术应用于潜标系统,能够为其携带的通信信标,提供无电气接触的充电功能。但由于信标与潜标的间距会发生变化,导致系统处于偏谐振状态,系统效率降低。本文首先分析耦合线圈随间距变化的规律,并对系统进行建模,分析系统偏谐振状态下线圈耦合效率与频率的关系;利用仿真验证了通过搜索算法调整工作频率,实现系统效率提升的可行性;建立线圈间距可变的系统样机,验证搜索算法对于系统效率提升的有效性。实验表明,当线圈间距变化时,搜索算法能够实现系统效率的提升。粒子群搜索算法的最大效率相对定步长扰动观测法提升3%,并且受到初始中心频率的选择影响较小,具有很好的鲁棒性,能够用于提升系统在线圈间距变化时的传输效率。

弹性飞机的混合H_2/H_∞最优PID控制器设计

针对弹性飞机强鲁棒性、低阶次、低增益鲁棒控制器的设计问题,以某弹性飞机12阶模型为研究对象,研究了基于弹性飞机降阶模型的混合H2/H∞最优PID控制器的设计。首先基于平衡截断法得到了6阶降阶模型。然后,根据全阶模型和降阶模型的频域降阶误差选取了合适的鲁棒加权函数。之后,给出了系统跟踪误差的H2范数的一种简化计算方法用于计算H2范数优化设计指标。最后使用粒子群优化算法进行了混合H2/H∞最优PID控制器参数的优化得到了最优PID控制器。仿真结果表明,与H∞混合灵敏度控制器相比,混合H2/H∞最优PID控制器阶次更低,并能同时镇定参数和非参数两种不确定性具有更强的鲁棒性;对弹性形变有较好的抑制作用,对刚性模态也取得了很好的控制效果。

基于粒子群算法的多目标柔性作业车间调度问题研究

对多目标柔性作业车间调度问题进行研究,分别以生产车间中的最大完工时间、机器总负载、加工生产成本和总拖期时间为性能指标,建立多目标柔性作业车间调度模型。针对多目标柔性作业车间调度问题的自身特点,设计一种扩展的基于工序编码及自动调度解码机制。考虑粒子最大、最小收敛速度及相应边界条件,设计一种应用于解决柔性生产调度的多目标粒子群算法。利用该算法求解柔性作业车间调度问题得到一组Pareto解集。通过基准实验测试与实际生产实例,验证该算法的可行性与有效性。

双电机独立驱动电动车辆电子差速控制

为了满足电动汽车的驱动要求,达到良好的控制效果,以永磁同步电动机矢量控制技术为基础,提出了一种两后轮独立电机驱动电动汽车的电子差速控制策略,建立了其M atlab/S im u link仿真模型,同时采用混合最优PSO算法对P I参数进行了优化。仿真结果显示,该策略能够使电机表现出很好的快速响应与平稳性,适合独立电机驱动电动汽车的驱动系统。

双馈风力发电机参数分步辨识及观测量的选择

参数辨识依赖于激励信号与观测量的选择。该文根据观测量的模式增量计算,确定系统动态在不同观测量上的可观性,据此选择观测量。根据不同扰动激发出的系统主导动态,确定在该扰动下的重要参数。采用分步辨识思路,先根据电网侧故障辨识双馈风力发电机电气部分参数,再基于输入侧风速变化辨识机械部分各参数。辨识方法采用全局最优位置变异粒子群算法,仿真算例验证了该方法的有效性。

Layout problem of multi-component systems arising for improving maintainability

To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration,an algorithm QCDSS(QoS constraints of dynamic Web services selection)to resolve dynamic Web services selection with QoS global optimal path,was proposed.The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints.The operations of the cross and mutation in genetic algorithm were brought into PSOA(particle swarm optimization algorithm),forming an improved algorithm(IPSOA)to solve the QoS global optimal problem.Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms.

基于Lbest PSO和NNs的电液伺服系统输出力PID控制研究

为了实现电液伺服系统输出力的稳定控制,结合局部最优粒子群优化算法和神经网络模型,提出一种PID控制器设计方法。该方法将神经网络模型(NNS)与PID控制器耦合,得到基于神经网络的PID控制器参数整定结构;再采用局部最优粒子群优化算法(Lbest PSO)确定神经网络的权重,从而得到基于局部最优粒子群优化算法和神经网络的PID控制算法;最后将提出的PID控制算法用于控制虚拟的电液伺服加载系统,以进行仿真实验。仿真结果表明,由该PID控制器控制的电液伺服系统的输出力平稳地收敛于给定力,从而提高了系统的稳定性。

高温超导比特磁体最优电流分布研究

研究了高温超导比特磁体最优电流分布的求解方法。在4.2 K下对高温超导比特磁体电流分布与载流能力的利用率进行了数值研究,采用有限元建模对粒子群(PSO)算法求解的最优电流分布进行分析。结果表明,高温超导比特磁体采用最优电流分布时,提高了磁体载流能力利用率,提高了磁体储能密度。

Optimal control of a spacecraft with deployable solar arrays using particle swarm optimization algorithm

The optimal control problem of the multibody dynamics of a spacecraft in space, modeled as a central body with one-sided connected deployable solar arrays, is investigated. The dynamical equations of motion of the spacecraft with solar arrays are derived using the multibody dynamics method. The control of the attitude motion of a spacecraft system can be transformed into the motion planning problem of nonholonomic system when the initial angular momentum is zero. These are then used to investigate the motion planning of the spacecraft during solar arrays deployment via particle swarm optimization (PSO) and results are obtained with the optimal control input and the optimal trajectory. The results of numerical simulation show that this approach is effective for the control problem of the attitude of a spacecraft during the deployment process of its solar arrays.

Modified constriction particle swarm optimization algorithm

To deal with the demerits of constriction particle swarm optimization(CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random velocity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likelihood of being trapped into local optima. Finally the convergence of the algorithm is verified by calculation examples.

Nonholonomic motion planning for a free-falling cat using spline approximation

An optimal motion planning of a free-falling cat based on the spline approximation is investigated.Nonholonomicity arises in a free-falling cat subjected to nonintegrable velocity constraints or nonintegrable conservation laws.The equation of dynamics of a free-falling cat is obtained by using the model of two symmetric rigid bodies.The control of the system can be converted to the motion planning problem for a driftless system.A cost function is used to incorporate the final errors and control energy.The motion planning is to determine control inputs to minimize the cost function and is formulated as an infinite dimensional optimal control problem.By using the control parameterization,the infinite dimensional optimal control problem can be transformed to a finite dimensional one.The particle swarm optimization(PSO) algorithm with the cubic spline approximation is proposed to solve the finite dimension optimal control problem.The cubic spline approximation is introduced to realize the control parameterization.The resulting controls are smooth and the initial and terminal values of the control inputs are zeros,so they can be easily generated by experiment.Simulations are also performed for the nonholonomic motion planning of a free-falling cat.Simulated experimental results show that the proposed algorithm is more effective than the Newtoian algorithm.