基于NPSO算法求解车间作业调度问题

研究车间生产生产调试系统,使资源达到优化配置,实现了一种基于小生境的粒子群优化算法用于求解车间作业调度问题。通过在粒子群算法中引入共享函数和共享适应度函数分别用来计算粒子间的共享度和粒子的共享适应值,并用粒子的共享适应值来反映其适应能力。粒子的位置越相近,则粒子间的共享度越大,相应粒子的共享适应值则越小。通过设置小生境半径的方式,将整个粒子群分解为多个小生境子种群,并通过设置小生境中的最大粒子个数参数,严格控制各个小生境中的粒子数量,使得所有粒子尽可能地分布到整个搜索空间的不同局部峰值区域,从而有效求得问题的全局最优值。仿真结果表明了算法对经典JSP问题求解的优良性能。     

A novel closed loop identification method and its application of multivariable system

Motivated by the fact that many classical identification methods cannot be directly used for closed loop control system for the excitation signal without the strict requirement, a novel identification method is proposed to simultaneously estimate model parameter and the structure. To improve the optimizing efficiency of the particle swarm, the global best particles are further optimized via a nonlinear stochastic search during the certain iteration of PSO and the novel method is denoted NPSO. On the basis, the frequency response estimation is introduced to provide a rough initial for the presented algorithm to improve the validity and accuracy. We simplify the complexity of the closed loop identification via the equivalent input and output. Simulation examples of SISO systems and MIMO systems are given to demonstrate the effectiveness and accuracy of the proposed algorithm.     

A decision-making method for personalized composite service

Web services are emerging technologies that enable application to application communication and reuse of autonomous services. Web services composition is a concept of integrating component services to conduct complex business transactions based on functionality and performance constraints. With the rapid growth of Web services on the Internet, the services owing the same functionality and different performance become more and more, and different users care for different quality. Therefore, designing effective decision-making method for the personalized composite service has become a fundamental problem for the application based on Web service. In this paper, oriented to the user need with single optimization objective, an efficient algorithm, which consists of Particle Swarm Optimization (PSO) and Niche technology, is presented to solve the Web service selection problem. Furthermore, in view of the practical composition requirements including multiple optimization objectives, an algorithm used to resolve the service selection with multi-objective multi-constraint is designed based on NPSO and the intelligent optimization theory of multi-objective PSO, which can produce a set of Pareto optimal composite services by means of optimizing various objective functions simultaneously. Experimental results show that NPSO algorithm owns better global convergence ability with faster convergence speed. Meanwhile, multi-objective multi-constraint NPSO is both feasible and efficient.     

A multivariable IMC-PID method for non-square large time delay systems using NPSO algorithm

Multiple time delays and strong interactions among different loops are the main problems in the design of multivariable controller for non-square systems. In this paper, the concept of effective open-loop transfer function (EOTF) is extended to non-square systems. By applying the internal model control (IMC) method, the controllers with equivalent models are designed. For practical applications, the NPSO algorithm is used to obtain the parameters of the incremental PID with first-order lag filter. This new method does not only avoid the complex computation caused by the procedure of decoupling first and then designing controllers but also employs the advantages of IMC-PID's suitable for large time delay systems and strong robustness. Simulation is carried out to demonstrate the effectiveness of the proposed method; also significant performance improvement has been achieved with the proposed method compared with other methods.     

基于物理规划与容差模型的压杆可靠性稳健设计

可靠性稳健设计本质上是一个多目标优化问题。物理规划法的偏好函数设计对数学模型有非常重要影响,为了使偏好区间的选择更加符合工程实际,文章将物理规划法与容差模型二步解法结合。第一步通过获取待优化目标函数的一般非稳健解信息,以此指导目标函数偏好函数的设计;第二步,构建以综合目标偏好函数为新的设计目标函数,将多目标优化问题转化为单目标问题,引入稳健设计变差影响,构建可靠性稳健设计模型;最后利用小生境粒子群算法对模型进行求解。以压杆稳定设计问题为例,验证了所提方法理论的正确性和可行性,表明该方法是解决工程构件可靠性稳健设计的一种实用有效的数值方法。     

外圆磨削在线优化

基于基本的磨削模型,利用小生境蚁群算法和小生境粒子群算法,对磨削进给量和磨削深度进行在线优化。并采用Matlab编程,利用声发射信号和优化模型,对磨削过程进行仿真,实现了外圆磨削实际磨削过程的在线监测和优化。对提高磨削质量和磨削效率,实现磨削过程的在线监测和智能化控制有着极大的推动作用。     

计入分布式垃圾发电的配网无功优化

研究了垃圾电站并入配网后的无功优化问题,提出了基于垃圾热值的发电数学模型,分为焚烧发电和沼气燃料电池发电两类.据此模型提出了一种新的配电网无功优化模型,该模型把垃圾处理量作为附加控制变量,把垃圾处理能力作为新的约束条件.在求解方法上,采用小生境粒子群算法进行求解,得到了含有垃圾发电的配网无功优化方案.算例表明,提出的垃圾发电模型和无功优化模型具有较好的准确性和实用性,对垃圾电站的进一步研究具有一定的参考价值,且垃圾发电较燃煤发电具有更好的经济性和环保性,也为垃圾发电在其它方面的应用提供了一些理论依据.     

小生境粒子群算法在多信号DOA估计中的应用

多重信号分类(Multiple Signal Classification,MUSIC)算法是波达方向(Direction of Arrival,DOA)估计领域中的经典算法之一,但其谱峰搜索过程的巨大计算量降低了算法的实时性.经典进化算法虽能降低搜索时间,却仅能搜索到一个解,当存在多个入射信号时便无法搜索全部解.为了...     

含风电场的输电网运营效率评估

针对传统含风电场的输电网运营效率评估体系中存在的指标冗余和评估结果准确性较差的问题,首先引用相关性分析法去除相关性较强的指标,随后运用组合权重法合理赋权突出主导因素,从而筛选精简出含有11个代表性极强的指标的合理的评估指标体系,由此构建出模糊神经网络综合评价体系。然而模糊神经网络在训练参数时收敛速度较慢且容易陷入局部最优解,因此文中在对比分析了梯度下降法、粒子群算法及小生境粒子群(NPSO)算法的收敛速度和精度后,选择具有最优收敛性能的NPSO算法求解最优参数并更新到神经网络中。测试数据表明,实际输出和预测输出的契合度很高,最终针对甘肃省L市、T市和B市2011—2016年的输电网运营效率进行评估与分析。