Minimum Normal Force Principle Based Quantitive Optimization of Clamping Forces for Thin Walled Part

Based on the stability criteria of workpiece-fixture system, quantitative optimization of clamping forces during precise machining process for thin walled part is studied considering the contact condition between wokpiece and locator, the contact mechanical model is achieved, which is further been used to calculate the entire passive forces acting on the statically undetermined workpiece by means of the force screw theory as well as minimum norm force principle. Furthermore, a new methodology to optimize clamping forces is put forward, on the criteria of keeping the stability of workpiece during cutting process. By this way, the intensity of clamping forces is decreased dramatically, which will be most beneficial for improving the machining quality of thin-walled parts. Finally, a case study is used to support and validate the proposed model.

NEW APPROACH FOR RELIABILITY-BASED DESIGN OPTIMIZATION:MINIMUM ERROR POINT

Conventional reliability-based design optimization （RBDO） requires to use the most probable point （MPP） method for a probabilistic analysis of the reliability constraints. A new approach is presented, called as the minimum error point （MEP） method or the MEP based method, for reliability-based design optimization, whose idea is to minimize the error produced by approximating performance functions. The MEP based method uses the first order Taylor＇s expansion at MEP instead of MPP. Examples demonstrate that the MEP based design optimization can ensure product reliability at the required level, which is very imperative for many important engineering systems. The MEP based reliability design optimization method is feasible and is considered as an alternative for solving reliability design optimization problems. The MEP based method is more robust than the commonly used MPP based method for some irregular performance functions.

基于Global optimization寻找无向完全图的最小生成树

将Global optimization思想引入到寻找无向完全图最小生成树的问题中,提出了Global optimization算法。与Kruskal算法和Prim算法相比之下,此算法避免了求解过程中对生成树中是否出现回路的判断,并在一定程度上降低了时间复杂度。

Topology optimization of shear wall structures under seismic loading

A topology optimization formulation is developed to find the stiffest structure with desirable material distribution subjected to seismic loads. Finite element models of the structures are generated and the optimality criteria method is modified using a simple penalty approach and introducing fictitious strain energy to simultaneously consider both material volume and displacement constraints. Different types of shear walls with/without opening are investigated. Additionally, the effects of shear wall-frame interaction for single and coupled shear walls are studied. Gravity and seismic loads are applied to the shear walls so that the definitions provide a practical approach for locating the critical parts of these structures. The results suggest new viewpoints for architectural and structural engineering for placement of openings.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Prediction of RNA Secondary Structure Based on Particle Swarm Optimization

A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization（PSO）. PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization（IPSO） was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to encoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.

Landscape Pattern Optimization of Chengdu Based on Modern Garden City

TOptimization of regional landscape pattern is significant for improving function and value of ecosystem,and restraining the expansion of urban layout.Taking Chengdu City for example,this paper applied RS and GIS techniques,landscape indexes and ecological service function evaluation to further analyze the temporal and spatial characteristics of landscape pattern and spatial differences of regional ecological functions,and on this basis,identified the spatial distribution of ecological source lands.Based on the long-term objective of building Chengdu into a modem garden city,this paper applied the accumulative cost distance model and introduced garden city theory to construct regional ecological corridors and ecological nodes,and explored the approaches of optimizing landscape pattern of modem garden city.The results showed that a great deal of arable land has been transferred to construction land in the urbanization;intensity of regional ecological functions showed obvious spatial differences;ecological source lands were mainly distributed in the Longmen Mountain,the Qionglai Mountain,the Changqiu Mountain and the Longquan Mountain;according to actual conditions of the study area,the road ecological corridors,river corridors and agricultural corridors in the layout of "four rings and six radial corridors" were constructed;ecological nodes dominated by intersection,wetland and forest park were formed.This research method and results are significant references for building Chengdu into a modem garden

Thermoelastic Structural Topology Optimization Based on Moving Morphable Components Framework

This study investigates structural topology optimization of thermoelastic structures considering two kinds of objectives ofminimumstructural compliance and elastic strain energy with a specified available volume constraint.To explicitly express the configuration evolution in the structural topology optimization under combination of mechanical and thermal load conditions,the moving morphable components(MMC)framework is adopted.Based on the characteristics of the MMC framework,the number of design variables can be reduced substantially.Corresponding optimization formulation in the MMC topology optimization framework and numerical solution procedures are developed for several numerical examples.Different optimization results are obtained with structural compliance and elastic strain energy as objectives,respectively,for thermoelastic problems.The effectiveness of the proposed optimization formulation is validated by the numerical examples.It is revealed that for the optimization design of the thermoelastic structural strength,the objective function with the minimum structural strain energy can achieve a better performance than that from structural compliance design.

Packet Optimization of Software Defined Network Using Lion Optimization

There has been an explosion of cloud services as organizations take advantage of their continuity,predictability,as well as quality of service and it raises the concern about latency,energy-efficiency,and security.This increase in demand requires new configurations of networks,products,and service operators.For this purpose,the software-defined network is an efficient technology that enables to support the future network functions along with the intelligent applications and packet optimization.This work analyzes the offline cloud scenario in which machines are efficiently deployed and scheduled for user processing requests.Performance is evaluated in terms of reducing bandwidth,task execution times and latencies,and increasing throughput.A minimum execution time algorithm is used to compute the completion time of all the available resources which are allocated to the virtual machine and lion optimization algorithm is applied to packets in a cloud environment.The proposed work is shown to improve the throughput and latency rate.

A Novel Hybrid Pan-Sharpen Method Using IHS Transform and Optimization

Intensity-hue-saturation (IHS) transform is the most commonly used method for image fusion purpose. Usually, the intensity image is replaced by Panchromatic (PAN) image, or the difference between PAN and intensity image is added to each bands of RGB images. Spatial structure information in the PAN image can be effectively injected into the fused multi-spectral (MS) images using IHS method. However, spectral distortion has become the typical factor deteriorating the quality of fused results. A hybrid image fusion method which integrates IHS and minimum mean-square-error (MMSE) was proposed to mitigate the spectral distortion phenomenon in this study. Firstly, IHS transform was used to derive the intensity image;secondly, the MMSE algorithm was used to fuse the histogram matched PAN image and intensity image;thirdly, optimization calculation was employed to derive the combination coefficients, and the new intensity image could be expressed as the combination of intensity image and PAN image. Fused MS images with high spatial resolution can be generated by inverse IHS transform. In numerical experiments, QuickBird images were used to evaluate the performance of the proposed algorithm. It was found that the spatial resolution was increased significantly;meanwhile, spectral distortion phenomenon was abated in the fusion results.

Shape-sizing nested optimization of deployable structures using SQP

The potential role of formal structural optimization was investigated for designing foldable and deployable structures in this work.Shape-sizing nested optimization is a challenging design problem.Shape,represented by the lengths and relative angles of elements,is critical to achieving smooth deployment to a desired span,while the section profiles of each element must satisfy structural dynamic performances in each deploying state.Dynamic characteristics of deployable structures in the initial state,the final state and also the middle deploying states are all crucial to the structural dynamic performances.The shape was represented by the nodal coordinates and the profiles of cross sections were represented by the diameters and thicknesses.SQP(sequential quadratic programming) method was used to explore the design space and identify the minimum mass solutions that satisfy kinematic and structural dynamic constraints.The optimization model and methodology were tested on the case-study of a deployable pantograph.This strategy can be easily extended to design a wide range of deployable structures,including deployable antenna structures,foldable solar sails,expandable bridges and retractable gymnasium roofs.

Modeling and Optimization of a Special Multistage Star Switching （SMSSS） System with SEs at Unequal Port Rates

There has been lack of an efficient design and evaluation method for the multistage star switching(MSSS) architecture in which the ports' rates of each switching element(SE) are unequal.Thus,we identify and propose a special MSSS(SMSSS) model for the first time,where all special SEs,known as basic switching modules(BSMs),are connected hierarchically into a tree profile.Unlike the existing investigations,each BSM in this model is characterized by one highrate port and several low-rate ports.This study focuses on the analysis,design and optimization of the SMSSS model.Moreover,we propose a novel BSM cost model which relates to its flux factor considered rarely in existing studies.Two examples are demonstrated to obtain the optimal structure parameters of the SMSSS system with a minimum overall cost.The comparison of the proposed SMSSS with similar fat tree structures indicates its relative advantages.

Gradient Gene Algorithm: a Fast Optimization Method to MST Problem

The extension of Minimum Spanning Tree(MST) problem is an NP hard problem which does not exit a polynomial time algorithm. In this paper, a fast optimization method on MST problem——the Gradient Gene Algorithm is introduced. Compared with other evolutionary algorithms on MST problem, it is more advanced: firstly, very simple and easy to realize; then, efficient and accurate; finally general on other combination optimization problems.

Interception time and uncertainty optimization for tangent-impulse orbit interception problem

The traditional tangent impulse interception problem does not consider the influence of actual deviation.However,by taking the actual state deviation of the interceptor into the orbit design process,an interception orbit that is more robust than the nominal orbit can be obtained.Therefore,we study the minimum time interception problem and the minimum terminal interception error problem under tangent impulse conditions and give an orbit optimization method that considers the interception time and the interception uncertainty.First,we express the interceptor's transfer time equation as a form of flight path angle,establish a global optimization model for solving the minimum time tangent impulse interception and give a hybrid optimization algorithm based on Augmented Lagrange Genetic Algorithm-Sequential Quadratic Programming(ALGA-SQP).Secondly,we use the universal time equation and Bootstrap resampling technology to calculate the interceptor's terminal error distribution and establish the relevant global optimization model by using the circumscribed cuboid volume of the interceptor's terminal position error ellipsoid as the optimization index.Finally,we combined the above two singleobjective optimization models to establish a global multi-objective optimization model that considers interception time and interception uncertainty and gave a hybrid multi-objective optimization algorithm based on Non-dominated Sorting Genetic Algorithm Ⅱ-Goal Achievement Method(NSGA2-GAM).The simulation example verifies the effectiveness of this method.

Minimum Ellipsiod and Optim alDesign

This paper deals with the constructing of optimal designs in regression with geometrical methods. It is proved that the covariance matrix of optimal design is fixed by its geometrical constructure. The necessary and sufficient conditions are given to construct optimal design.

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things(IoT).The internet can connect the huge amount of data,which contains lot of processing operations and efforts to transfer the pieces of information.The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics,sensors and network connectivity.Nowadays,there are two trending technologies that take the platform i.e.,Software Defined Network(SDN)and IoT(SD-IoT).The main aim of the IoT network is to connect and organize different objects with the internet,which is managed with the control panel and data panel in the SD network.The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers.It is more significant for wide area networks,because of the large packet propagation latency and the controller placement problem is more important in every network.In the proposed work,IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization(ESFO)algorithm and Pareto Optimal Controller placement tool(POCO)for the placement problem of the controller.In order to prove the efficiency of the proposed system,it is compared with other existing methods like PASIN,hybrid SD and PSO in terms of load balance,reduced number of controllers and average latency and delay.With 2 controllers,the proposed method obtains 400 miles as average latency,which is 22.2%smaller than PSO,76.9%lesser than hybrid SD and 91.89%lesser than PASIN.

Vertex Cover Optimization Using a Novel Graph Decomposition Approach

The minimum vertex cover problem(MVCP)is a well-known combinatorial optimization problem of graph theory.The MVCP is an NP(nondeterministic polynomial)complete problem and it has an exponential growing complexity with respect to the size of a graph.No algorithm exits till date that can exactly solve the problem in a deterministic polynomial time scale.However,several algorithms are proposed that solve the problem approximately in a short polynomial time scale.Such algorithms are useful for large size graphs,for which exact solution of MVCP is impossible with current computational resources.The MVCP has a wide range of applications in the fields like bioinformatics,biochemistry,circuit design,electrical engineering,data aggregation,networking,internet traffic monitoring,pattern recognition,marketing and franchising etc.This work aims to solve the MVCP approximately by a novel graph decomposition approach.The decomposition of the graph yields a subgraph that contains edges shared by triangular edge structures.A subgraph is covered to yield a subgraph that forms one or more Hamiltonian cycles or paths.In order to reduce complexity of the algorithm a new strategy is also proposed.The reduction strategy can be used for any algorithm solving MVCP.Based on the graph decomposition and the reduction strategy,two algorithms are formulated to approximately solve the MVCP.These algorithms are tested using well known standard benchmark graphs.The key feature of the results is a good approximate error ratio and improvement in optimum vertex cover values for few graphs.

Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm

A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrangement of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum.A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations.Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is feasible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the algorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.

Optimization of Geometry at Hartree-Fock level Using the Generalized Simulated Annealing

This work presents a procedure to optimize the molecular geometry at the Hartree-Fock level, based on a global opti-mization method—the Generalized Simulated Annealing. The main characteristic of this methodology is that, at least in principle, it enables the mapping of the energy hypersurface as to guarantee the achievement of the absolute minimum. This method does not use expansions of the energy, nor of its derivates, in terms of the conformation variables. Distinctly, it performs a direct optimization of the total Hartree-Fock energy through a stochastic strategy. The algorithm was tested by determining the Hartree-Fock ground state and optimum geometries of the H2, LiH, BH, Li2, CH+, OH?, FH, CO, CH, NH, OH and O2 systems. The convergence of our algorithm is totally independent of the initial point and do not require any previous specification of the orbital occupancies.

<i>ε</i>-Optimality in Multivalued Optimization

In this paper we apply the directional derivative technique to characterize D-multifunction, quasi D-multifunction and use them to obtain ε-optimality for set valued vector optimization problem with multivalued maps. We introduce the notions of local and partial-ε-minimum (weak) point and study ε-optimality, ε-Lagrangian multiplier theorem and ε-duality results.