基于网络教学平台的信息技术“任务单”教学——以《排序算法》一课为例

随着信息化时代的发展,信息技术教学的形式也渐渐趋向网络化、平台化。将“任务单”教学模式与“教学助手”的各教学模块有机结合,实施信息技术教学。

Personalized web pages ranking algorithm based on user preferences

In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.

New rank learning algorithm

To overcome the limitation that complex data types with noun attributes cannot be processed by rank learning algorithms, a new rank learning algorithm is designed. In the learning algorithm based on the decision tree, the splitting rule of the decision tree is revised with a new definition of rank impurity. A new rank learning algorithm, which can be intuitively explained, is obtained and its theoretical basis is provided. The experimental results show that in the aspect of average rank loss, the ranking tree algorithm outperforms perception ranking and ordinal regression algorithms and it also has a faster convergence speed. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

HEURISTIC STUDY OF FLOWSHOP SCHEDULING TO MINIMIZE MEAN FLOW TIME WITH LOT TRANSFER CONSIDERED

Lot scheduling problem with idle time transfer between processes to minimize mean flow time is very important because to minimize mean flow time is to minimize work in process. But the problem is NP hard and no polynomial algorithm exists to guarantee optimal solution. Based the analysis the mathematical structure of the problem, the paper presents a new heuristic algorithm. Computer simulation shows that the proposed heuristic algorithm performs well in terms of both quality of solution and execution speed.

An Image Encryption Approach Using a Shuffling Map

A new image encryption approach is proposed.First,a sort transformation based on nonlinear chaoticalgorithm is used to shuffle the positions of image pixels.Then the states of hyper-chaos are used to change the greyvalues of the shuffled image according to the changed chaotic values of the same position between the above nonlinearchaotic sequence and the sorted chaotic sequence.The experimental results demonstrate that the image encryptionscheme based on a shuffling map shows advantages of large key space and high-level security.Compared with someencryption algorithms,the suggested encryption scheme is more secure.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

Optimization of maintenance strategy for high-speed railwaycatenary system based on multistate model

A multi-objective optimization model considering both reliability and maintenance cost is proposed to solve the contradiction between reliability and maintenance cost in high-speed railway catenary system maintenance activities.The non-dominated sorting genetic algorithm 2(NSGA2)is applied to multi-objective optimization,and the optimization result is a set of Pareto solutions.Firstly,multistate failure mode analysis is conducted for the main devices leading to the failure of catenary,and then the reliability and failure mode of the whole catenary system is analyzed.The mathematical relationship between system reliability and maintenance cost is derived considering the existing catenary preventive maintenance mode to improve the reliability of the system.Secondly,an improved NSGA2(INSGA2)is proposed,which strengths population diversity by improving selection operator,and introduces local search strategy to ensure that population distribution is more uniform.The comparison results of the two algorithms before and after improvement on the zero-ductility transition(ZDT)series functions show that the population diversity is better and the solution is more uniform using INSGA2.Finally,the INSGA2 is applied to multi-objective optimization of system reliability and maintenance cost in different maintenance periods.The decision-makers can choose the reasonable solutions as the maintenance plans in the optimization results by weighing the relationship between the system reliability and the maintenance cost.The selected maintenance plans can ensure the lowest maintenance cost while the system reliability is as high as possible.

Integration optimization of novel electric power steering system based on quality engineering theory

The dynamic model of a novel electric power steering （EPS） system integrated with active front steering function （the novel EPS system） is built. The concepts and quantitative expressions of the steering road feel, steering sensibility, and steering operation stability are introduced. Based on quality engineering theory, the optimization algorithm is proposed by integrating the Monte Carlo descriptive sampling, elitist non-dominated sorting genetic algorithm （NSGA-II） and 6-sigma design method. With the steering road feel and the steering portability as optimization targets, the system parameters are optimized by the proposed optimization algorithm. The simulation results show that the system optimized based on quality engineering theory can improve the steering road feel, guarantee steering stability and steering portability and thus provide a theoretical basis for the design and optimization of the novel electric power steering system.

Method for optimizing manipulator's geometrical parameters and selecting reducers

A geometrical parameters optimization and reducers selection method was proposed for robotic manipulators design. The Lagrangian approach was employed in deriving the dynamic model of a two-DOF manipulator. The flexibility of links and joints was taken into account in the mechanical structure dimensions optimization and reducers selection, in which Timoshenko model was used to discretize the hollow links. Two criteria, i.e. maximization of fundamental frequency and minimization of self-mass/load ratio, were utilized to optimize the manipulators. The NSGA-II (fast elitist nondominated sorting genetic algorithms) was employed to solve the multi-objective optimization problem. How the joints flexibility affects the manipulators design was analyzed and shown in the numerical analysis example. The results indicate that simultaneous consideration of the joints and the links flexibility is very necessary for manipulators optimal design. Finally, several optimal combinations were provided. The effectiveness of the optimization method was proved by comparing with ADAMS simulation results. The self-mass/load ratio error of the two methods is within 10%. The maximum error of the natural frequency by the two methods is 23.74%. The method proposed in this work provides a fast and effective pathway for manipulator design and reducers selection.

Theoretical and experimental investigation on optimization of a non-contact air conveyor

Air film conveyors equipped with porous pads have been developed to bring the liquid crystal display(LCD) into a non-contact state during transportation process. In this work, a theoretical model including flow property of porous media and Reynolds equation is established within a representative region in order to optimize the design parameters of a partial porous air conveyor. With the theoretical model, an optimization method using nondominated sorting genetic algorithm – II(NSGA-II) is applied for a two-objective optimization to achieve a minimum air consumption and maximum load capacity. Three Pareto-optimal solutions are selected to analyze the influence of each parameter on the characteristics of the air conveyor, and the results indicate that the position of the porous pads has the most significant impact on the performance and of course must be determined with care. Furthermore, experimental results in terms of the supporting force versus gap clearance show that the optimized air conveyor can greatly improve the load capacity over the normal one, indicating that the optimization method is applicable for practical use.

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Optimal transmission lines assignment with maximal reliabilities in multi-source multi-sink multi-state computer network

The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.

Investigation on Optimization Design of an Equivalent Water Depth Truncated Mooring System Based on INSGA-Ⅱ

At present,equivalent water depth truncated mooring system optimization design is regarded as the priority of hybrid model testing for deep sea platforms,and will replace the full depth system test in the future.Compared with the full depth system,the working depth and span are smaller in the truncated one,and the other characteristics maintain more consistency as well.In this paper,an inner turret moored floating production storage ＆ offloading system（FPSO） which works at a water depth of 320m,was selected to be a research example while the truncated water depth was 80m.Furthermore,an improved non-dominated sorting genetic algorithm（INSGA-II） was selected to optimally calculate the equivalent water depth truncated system,considering the stress condition of the total mooring system in both the horizontal and vertical directions,as well as the static characteristic similarity of the representative single mooring line.The results of numerical calculations indicate that the mathematical model is feasible,and the optimization method is fast and effective.

Fast and accurate calculation of seismic wave travel time in 3D TTI media

The Tilted tilted transversely isotropic(TTI)media,a kind of anisotropic medium,widely exists within the earth.For faster calculation of travel times in the TTI anisotropic media,we modifi ed a minimum traveltime tree algorithm with high effi ciency by dynamical modifi cation of the secondary wave propagation region during the spread of seismic waves.To manage the wavefront points in the modified version,we used a novel minimum heap sorting technique to reduce the time spent on selecting secondary waves points.In this study,seismic group velocities were obtained from analytical solutions in terms of phase angle,and the corresponding phase angles were determined by binary search rather than approximate equations for weakly anisotropic media.For the most time-consuming part of the secondary wave traveltime calculation,the parallel computation was initially performed using multiple cores and threads.Numerical examples showed that the improved method can calculate seismic travel times and ray paths faster and accurately in a 3D TTI medium.For four cores and eight threads,the computing speed increased by six times when compared to the conventional method.

NSGA Ⅱ based multi-objective homing trajectory planning of parafoil system

Homing trajectory planning is a core task of autonomous homing of parafoil system.This work analyzes and establishes a simplified kinematic mathematical model,and regards the homing trajectory planning problem as a kind of multi-objective optimization problem.Being different from traditional ways of transforming the multi-objective optimization into a single objective optimization by weighting factors,this work applies an improved non-dominated sorting genetic algorithm Ⅱ(NSGA Ⅱ) to solve it directly by means of optimizing multi-objective functions simultaneously.In the improved NSGA Ⅱ,the chaos initialization and a crowding distance based population trimming method were introduced to overcome the prematurity of population,the penalty function was used in handling constraints,and the optimal solution was selected according to the method of fuzzy set theory.Simulation results of three different schemes designed according to various practical engineering requirements show that the improved NSGA Ⅱ can effectively obtain the Pareto optimal solution set under different weighting with outstanding convergence and stability,and provide a new train of thoughts to design homing trajectory of parafoil system.

OPTIMIZATION ON ANTENNA PATTERN OF SPACEBORNE SAR WITH IMPROVED NSGA-Ⅱ

Optimization of antenna array pattern used in a spaceborne Synthetic Aperture Radar (SAR) system is considered in this study. A robust evolutionary algorithm, Non-dominated Sorting Genetic Algorithms (the improved NSGA-Ⅱ), is applied on a spaceborne SAR antenna pattern design. The system consists of two objective functions with two constraints. Pareto fronts are generated as a result of multi-objective optimization. After being validated by a test problem ZDT4, the algorithms are used to synthesize spaceborne SAR antenna radiation pattern. The good results with low Ambi- guity-to-Signal Ratio (ASR) and high directivity are obtained in the paper.

Optimal design of functionally graded Pm PV/CNT nanocomposite cylindrical tube for purpose of torque transmission

Carbon nanotube(CNT)/polymer nanocomposites have vast application in industry because of their light mass and high strength. In this work, a cylindrical tube which is made up of functionally graded(FG) PmP V/CNT nanocomposite, is optimally designed for the purpose of torque transmission. The main confining parameters of a rotating shaft in torque transmission process are mass of the shaft, critical speed of rotation and critical buckling torque. It is required to solve a multi-objective optimization problem(MOP) to consider these three targets simultaneously in the process of design. The three-objective optimization problem for this case is defined and solved using a hybrid method of FEM and modified non-dominated sorting genetic algorithm(NSGA-II), by coupling two softwares, MATLAB and ABAQUS. Optimization process provides a set of non-dominated optimal design vectors. Then, two methods, nearest to ideal point(NIP) and technique for ordering preferences by similarity to ideal solution(TOPSIS), are employed to choose trade-off optimum design vectors. Optimum parameters that are obtained from this work are compared with the results of previous studies for similar cylindrical tubes made from composite or a hybrid of aluminum and composite that more than 20% improvement is observed in all of the objective functions.

Multi-objective Scheduling Using an Artificial Immune System

Production scheduling is one of the most important problems to be considered in the effective performance of the automatic manufacturing system.It is the typical kind of NP-complete problem. The methods commonly used are not suitable to solve complicated problems because the calculating time rises exponentially with the increase of the problem size. In this paper, a new algorithm - immune based scheduling algorithm (IBSA) is proposed. After the description of the mathematics model and the calculating procedure of immune based scheduling,some examples are tested in the software system called HM IM& C that is developed usingVC+ +6.0. The testing results show that IBSA has high efficiency to solve scheduling problem.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAⅡ

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimal design of the automotive energy absorbing components. Modified non-dominated sorting genetic algorithm II(NSGA II) was used for multi-objective optimization of automotive S-rail considering absorbed energy(E), peak crushing force(Fmax) and mass of the structure(W) as three conflicting objective functions. In the multi-objective optimization problem(MOP), E and Fmax are defined by polynomial models extracted using the software GEvo M based on train and test data obtained from numerical simulation of quasi-static crushing of the S-rail using ABAQUS. Finally, the nearest to ideal point(NIP)method and technique for ordering preferences by similarity to ideal solution(TOPSIS) method are used to find the some trade-off optimum design points from all non-dominated optimum design points represented by the Pareto fronts. Results represent that the optimum design point obtained from TOPSIS method exhibits better trade-off in comparison with that of optimum design point obtained from NIP method.