Job-Shop scheduling based on improved immune cloning algorithm

Parallel immune clone algorithm is proposed based on population coevolution theory and parallel computing affinity of individual at multiple compute nodes. Introducing the immune memory mechanism, the evolution processes of antibody population and memory units are conducted simultaneously, meanwhile, it improves mutual cooperation among antibodies, and ensures solution set approaching optimal solution from the inside of feasible region or infeasible region border. Clone proliferation, high frequency variation and operation of crossover operators increase the chance that better individuals gain affinity maturation by the operation of clone expansion, improve diversity of antibody population distribution, achieve the balance of optimization between depth and range, and ensure the convergence of the algorithm and the diversity of the search range. A computational study for a standard data set is carried out to test the validity of the algorithm, and the effect of algorithm parameters on the results is analyzed. The simulation results show that the global search capability, local search capability, algorithm stability and computing speed of the algorithm are all superior to conventional optimization algorithms such as normal immune clone optimization algorithm, genetic algorithm, etc.     

Optimal model and algorithm for flexible job-shop scheduling problem based on demand time window

Aiming at the scheduling problem for jit delivery under the pulling type production, an optimal method for flexible job-shop scheduling problem (FJSP) based on demand time window is proposed. According to the demand time window of every workpiece, an optimal model for FJSP is constructed to maximize the average membership degree based on the completion time for every workpiece.Then, a tabu search algorithm based on multi-stage hybrid mutation is proposed and designed.To improve the convergence of the algorithm, inverse order mutation and gene segment swap mutation are used to find a good original solution, upon which gene swap mutation is used to find the optimal solution. The effectiveness of the proposed method is validated by applying to a multi-type and small-batch production manufacturing workshop.     

Dynamic scheduling method of vehicle air-conditioning assembly shop based on feedback control

By analyzing the inherent uncertainties, discrete uncertainties and external environmental uncertainties, a dual-feedback control strategy and the dynamic scheduling method are proposed to solve the dynamic scheduling problem of vehicle air-conditioning assembly shop under uncertainty. Considering the reentrant constraint, equipment capacity constraint, and time discrete constraint etc., the multi-objective scheduling model of vehicle air-conditioning assembly shop is set up,taken the earliness/tardiness, process time, production cost and quality loss as optimization objectives. According to the characteristic of the model, sectional chromosome encoding, crossover operator and mutation operator are designed to prevent the generation of illegal solutions and improve efficiency of the algorithm. An example is given to prove the feasibility and validity of the approach.     

Parallel algorithm for association rules in cluster

Based on comparing with the existing parallel algorithms for mining association rules, the paper analyzes CD,FDM,DDM algorithm in details. To overcome problems existing in efficiency of candidate sets pruning and execution time of these algorithms, the paper proposes parallel process algorithm with an improved parallel association rules. Finally the result of test on share-nothing cluster of workstation show that after adopting the improved algorithm, execution performance and efficiency are higher than others. This result indicates that the algorithm has achieved algorithm optimization.     

Improved DDR algorithm in media stream scheduling

Fuzzy control variable weight method of multi-factor decision-making adaptive deficit round robin scheduling algorithm (VWADRR), an improved deficit round robin(DRR) scheduling algorithm is put forward. It’s used in the circumstance of scheduling multiple multi-media data streams. It bases on multi-factor decision-making in the fuzzy control theory, specific to the use of the variable weight method. It’s adaptive for output channel bandwidth and buffer size of each input queues. Variable weight method makes decisions based on these two factors, that is, to determine the QN as variable in DRR. Considering the time of each packet sent, it reduces the packet delay. It effective uses buffers and bandwidth in the heavy network traffic situation, significantly reduces the possibility of buffer depletion, also smoothes burst.     

An application based on container programming and genetic algorithm

With the rapid growth of curriculums in colleges and universities and the number of students,automatic course arrangement method based on calculation algorithm has become an important issue of algorithm design and research. Based on Genetic Algorithm, a new binary encoding mechanism and classroom position filling method is put forward, which is applied to improve the load and efficiency of course scheduling system. In accordance with the ratio of weights in Fitness Function, this method abandons the way of random search, directionally and randomly generates chromosomes, simplifies the quantity of chromosome information, greatly shortens the running time of the system. In the aspect of management mechanism, user intervention is introduced,dealing with initial population with high proficiency and eliminating conflicts. Data from simulation experiment proves the astringency and high efficiency of this method. Moreover, the whole design of the system applies programming technology based on container, realizing the convenient processing of uncertain data.     

Dynamical Programming Algorithm for Multilayer feedforward Neural Networks

This paper trains the multilayer neural networks using dynamical programming.The weithts are adjusted layer by layer. The optimal weishts of each layer are calculated using OBPalgorithm proposed in the related reference. The globally optimal weights of each layer can be obtained by finite iterations.     

Simulation method for multi-machine and multi-task crane scheduling in steelmaking plant

It is of great significance in steel complex to link production flow between work stations and control production rhythm by cranes. Crane scheduling is a multi-machine multi-task problem which limits temporal and spatial constraints and production plan. A simulation model based on evolution rules to solve temporal and spatial constraints is proposed. Its objective is to guarantee execution of production plan in steelmaking plant. And match between cranes and production task on working span is conducted to fulfill temporal constraints between multi-tasks and spatial constraints among multi-cranes. Simulation rules including task allocation, conflicts eliminating and task finishing are also proposed to dominate simulation process. This model avoids difficulties of theoretical calculation for crane scheduling problem. Offline test in a steelmaking plant shows that it is available to make a crane scheduling without conflicts between cranes and plans, which provides a guideline for production scheduling.     

Path planning in 3-D space for robot based on ant colony algorithm

In this paper the ant colony algorithm is applied to path planning problem in 3-D space for robot. First of all, the space between the origin where the robot is located and destination was divided into a three dimensional grid, at the same time the valid path from origin to destination is defined, then all the ants will leave from the origin, selecting next vertex independently in three dimensional grid, to the destination on a valid path. The simulated experiment shows that the algorithm is efficient and has a great convergence rate.     

A contour extraction algorithm for digital images based on water balloons snake model

Due to weak edges and low contrast areas in digital images, such as a left ventricle in Magnetic Resonance Imaging (MRI) images, a deformation curve may leak from the outer boundary when images are segmented using the traditional snake model. An improved algorithm based on the water balloons snake model is introduced to solve this problem. Firstly, the barycenter and the boundary shape area of the left ventricle in a digital MRI image are obtained through mathematical morphology. The left ventricular watershed line is obtained next using a watershed transform algorithm, which is taken as the initial deformation curve in a balloon snake model to capture the contour. Different snake models are tested to digitally tagged MRI images of left ventricles of small pigs. Contrast results indicate that the water balloons snake model can solve the leakage problem in traditional snake and balloon snake models. In addition, the processing speed is improved significantly.     

BP neural network and genetic algorithm for thefilling properties optimization of crankshaft

The structure of wedge flash is proposed to improve the filling properties for deep cavity structure of crankshaft die with dimensional splitting mold. BP genetic algorithm is applied to optimize the structure parameters of wedge flash based on Matlab. Samples which are selected by orthogonal test are analyzed via FEM, and the minimum unfilled distance obtained are employed to conduct the BP neural network training. Then the optimum parameters with minimum unfilled distance are gained from genetic algorithm. Error between the parameters predicted and the results get from simulations is less than 5%. The productive practice indicates that the cavity is fully filled and the material utilization ratio increases from 75.7% to 81.4%, which confirms the correctness of optimization of wedge flash structure.     

Improved algorithm of contact element model for earthquake-induced pounding between structures

To better study the issue, we presented an improved algorithm. In this algorithm, the impact issue is equivalent to a spring〖XC半字线.tif,JZ〗oscillator model which contains an oscillator, a spring and a damper. The oscillator movement represents the compression phase and restitution phase of impact. By solving the motion equation, we got the penetration displacement,penetration velocity, impact force, impact time and their mutual relationship. The solution process did not carriy out the hypothesis that the energy dissipation or absolute speed during the restitution phase is the same as that during the compression phase, which was adopted by common solution methods. Therefore we can get the exact results or approximate results that are infinitely close to the exact results. In the case of small energy dissipation during the collision, Hertzdamp model results obtaind by common solution methods are close to the exact results. When the energy dissipation during the collision is large, the difference between them becomes obvious. Regardless of small or large energy dissipation during the collision, the improved algorithm can obtain the results which are infinitely close to the exact results. 〖BP(〗Therefore, the improved algorithm has more advantages than common solution methods, especially for solving the earthquake-induced pounding problems with a large number of energy dissipation during the collision.〖BP)〗     

A multi-parameter weighted clustering algorithm for mobile ad hoc networks

Network clustering can support resource spatial multiplexing and increase the capacity of the wirless networks. Clusterheads constitute the virtual backbone of the network, which reduces the overhead of routing and makes it simpler and easier for network     

BP neural network and SQP algorithm for the optimization of wheel die cavity

A method combining forging numerical simulation,BP neural network and SQP algorithm is developed to optimize the die cavity and reduce the wear of top die core,so as to improve die life. Optimal mathematical model is established for the purpose of equal wear and cubic spline interpolation curve is used to describe the shape of the forming part of top die core based on MATLAB. Then BP neural network is trained by the results of forging numerical simulation and modified Archard wear model to establish the relation between the control point of die cavity and the objective function. Finally,SQP algorithm is used to optimize the design variables and get the best shape of top die core. The results show that the wear of top die core decreases and becomes more uniform. The uniformity wear is reduced by 38.4% after optimization.     

Analysis of frictional contact problem for curved surface coupling with velocity using finite element algorithm

The mixed finite element algorithm is used to solve contact problems coupling with friction decided by velocity for curved surface in elastic field. The computational procedure is designed with the aid of Fortran language, and the technique is employed to account for the contact problems of one pair of meshing teeth in the plane stress and quasi static situation. The result indicates that the influence of velocity on friction affects the distribution of contact force as well as the meshing node pair’s contact status, and the consideration of the relation between velocity and friction is essential for the contact problem solutions. Ultimately the example is validated by the Ansys software.     

The method for removing the spurious modes of eigensystem realization algorithm

Due to the influence of spurious modes on the eigensystem realization algorithm results,singular value decomposition(SVD) and model energy level are introduced to remove the spurious modes of eigensystem realization algorithm,reduce part of the noise modes and improve the accuracy by reducing measurement noise by SVD. The energy matrix of each mode can be calculated by the selection matrices,the eigenvalues and eigenvectors of the state matrix and the input distribution matrix. The largest singular value of the energy matrix obtained by SVD is a measure for the energy contribution of each mode,which is named mode energy level. Spurious modes resulting from noise or model redundancy are indicated according their mode energy level. A numerical example and an experimental example are presented to demonstrate the efficacy of the method.     

A forward algorithm for prestressed construction process of large-span suspendome based on the equivalent pre-tension

To accurately simulate prestressed construction process of large-span suspendome, the initial state of construction process according to practical construction sequence is established, and the construction mechanical analysis for prestressed construction process is especially studied. Features of prestressed construction process of large-span suspendome are summarized, and the insufficiencies of the application of state variable superposition method, back analysis method, birth and death element method are analyzed. Combined with nonlinear finite element analysis method, forward algorithm for prestressed construction process based on the equivalent pre-tension is proposed, and the specific application method and procedure are given. Changzhou stadium steel roof engineering is taken as an example to verify the practicability and applicability of forward algorithm for prestressed construction process. The conclusion is that, using forward algorithm for prestressed construction process based on the equivalent pre-tension, the structure state of each construction phase can be accurately tracked, and the nonlinear contact of strut and rigid framework, interactive impact of cable force, conversion of temporary structure system, and structural geometric nonlinearity can be comprehensively considered.     

模式识别的斜拉桥损伤诊断动力指纹与识别算法

为有效并准确诊断出斜拉桥损伤,对基于模式识别的斜拉桥损伤诊断方法进行了研究。选取易于测试出的低阶模态频率和部分关键点竖向振型数据为动力指纹,无需模态扩展或模型缩聚。研究并采用全因子设计进行动力指纹库的创建,可精确评估设定的损伤因子及其交互作用对损伤识别结果的影响。设计并增加了带随机误差的动力指纹库样本集。编制了基于Matlab的模式识别的多种算法,重点研究了精确度高的多层感知器识别算法及其提高该算法预测准确率的装袋集成算法。最后给出一座单塔双跨双索面斜拉桥的多种识别算法的损伤诊断过程和结果,得到一种可包容测试随机误差的高精确度斜拉桥损伤诊断评估模型。     

Reliability evaluation algorithm for complex electrical distribution networks using the storage structure of adjacency multilist

A section algorithm of reliability evaluation for complex medium voltage Electrical Distribution Networks (EDNs) using adjacency multilist (AM) is proposed. The constructing method for AM is introduced. The AM can be used to store the structure data of EDNs and to solve the problem of space & time complexity. A new power flow algorithm of backward / forward sweep is proposed and a fast algorithm of forming sections in distribution network is put forward. The proposed algorithm makes a perfect combination of the power flow algorithm and the section algorithm of reliability evaluation, which makes efforts to decrease the work of programming and saves the computing time. Applying the proposed algorithm in RBTS-BUS6 system and actual EDNs, the reliability assessment results show that the algorithm has an advantage of saving time and possesses efficiency in engineering practicality.