A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION

This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO)approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the problem is obtained and a new SQO-type method is proposed,namely,splitting feasible SQO(SF-SQO)method.Moreover,under suitable conditions,we analyse the global convergence,strong convergence and rate of superlinear convergence of the SF-SQO method.Finally,preliminary numerical experiments regarding the economic dispatch of a power system are carried out,and these show that the SF-SQO method is promising.

Development of hybrid optimization algorithm for structures furnished with seismic damper devices using the particle swarm optimization method and gravitational search algorithm

Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.

An optimization method for passive muzzle arc control devices in augmented railguns

In this paper,a passive muzzle arc control device(PMACD)of the augmented railguns is studied.By discussing its performance at different numbers of extra rails,a parameter optimization model is proposed.Through the calculation model,it is found that the PMACD works well in the simple railgun,which refers to the gun that there is only one pair of rails in the inner bore.The PMACD may decrease the simple railgun’s armature peak current and muzzle arc,but affect its muzzle velocity not much.However,in the augmented railguns it has different characteristics.If the parameters of the PMACD are not selected suitable.It may increase the armature peak current and muzzle arc,but greatly decrease the velocity.The reason for this problem is that the extra rails generate a strong magnetic field in front of the armature,which induces a large current to change the armature current.It is also found that when the resistance and inductance parameters of the PMACD satisfy with the optimization formula,the PMACD can also play a good role in arc suppression in the augmented railguns.Experiments of an augmented railgun with a stainless steel PMACD are carried out to verify this optimization method.Results show that the muzzle arc is obviously controlled.This work may provide a reference for the design of the muzzle arc control device.

Analysis of Effective Optimization Methods for Building Construction Technology Management

During the construction process of the construction project,the construction technology management work can improve the overall quality of the project construction.In the context of increasingly fierce competition in the construction market,construction enterprises should strengthen the management of construction technology,enhance their technical level and market competitiveness,and promote the development of the construction market[1].The paper mainly analyzes the optimization methods of build-Keywords:ing construction technology management.

ANN Optimization Method for High Strength High Fracture Toughness Steels

The two-target optimization problem for high strength high fracture toughness steels has been investigated. An effective method for two-target optimization of multi-variable non-linear complicated system is developed by combining simulated annealing algorithm with artificial neural

Sub-region Alopex Optimization Method with RSM for Design of Permanent Magnet Machines

Based on an Alopex optimization algorithm and a response surface model(RSM),a hybrid sub-region methodology is presented to solve the optimal design problems of permanent magnet(PM)machines.The Alopex optimization method is processed both in subspace and in global solution space.In order to decrease the computing time,a multi quadric radial basis function(MQRBF)is embedded in the optimization.The proposed method speeds up the convergence rate while keeps the accuracy of the solution.A numerical experiment is given to validate the efficiency and effectiveness of the method.

A new two-axis solar tracker based on the online optimization method:Experimental investigation and neural network modeling

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun without using its movement model.In this research,four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles,which determine the position of the sun.The magnitude of the sunray is considered as the cost function of all algorithms.Then,several experiments are carried out to find the best optimization algorithm with optimal population size,number of iterations,and also the best initialization method.Uniform initialization leads to faster convergence compared to random initialization.The results clearly show that the particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms,with a convergence time of less than 40 s.The average fitness value or voltage received by the tracker is 2.4 Volts in this method,which is higher than other methods.TLBO also performs well with a population size of 15 and 7 iterations.Afterward,the artificial neural network with one hidden layer and 20 neurons is employed to predict these two parameters in each day and moment in a year in Shiraz city according to the experimental data extracted from PSO.Number of the day from January and the time are inputs and zenith and azimuth angles are considered the output of neural network modeling.The performance of the proposed ANN model is evaluated using regression plots,demonstrating a strong correlation between predicted and target outputs.Finally,the outcomes reveal the feasibility of using online optimization algorithms and neural network modeling in an effort to bypass the complex mathematical model of mechatronic systems and predict the movement of the sun automatically.

An Efficient Optimization Design Framework for Low-Resistance Shape of Bionic Amphibious Robot

To realize the low-resistance shape optimization design of amphibious robots,an efficient optimization design framework is proposed to improve the geometric deformation flexibility and optimization efficiency.In the proposed framework,the free-form deformation parametric model of the flat slender body is established and an analytical calculation method for the height constraints is derived.CFD method is introduced to carry out the high-precision resistance calculation and a constrained Kriging-based optimization method is built to improve the optimization efficiency by circularly infilling the new sample points which satisfying the constraints.Finally,the shape of an amphibious robot example is optimized to get the low-resistance shape and the results demonstrate that the presented optimization design framework has the advantages of simplicity,flexibility and high efficiency.

Optimization Methods for Mixed Integer Weakly Concave Programming Problems

In this paper,we consider a class of mixed integer weakly concave programming problems(MIWCPP)consisting of minimizing a difference of a quadratic function and a convex function.A new necessary global optimality conditions for MIWCPP is presented in this paper.A new local optimization method for MIWCPP is designed based on the necessary global optimality conditions,which is different from the traditional local optimization method.A global optimization method is proposed by combining some auxiliary functions and the new local optimization method.Furthermore,numerical examples are also presented to show that the proposed global optimization method for MIWCPP is efficient.

Economic Dispatch of Power Systems with Virtual Power Plant Based Interval Optimization Method

Load prediction and power prediction uncertainties are inevitable aspects of a virtual power plant(VPP).In power system economic dispatch(ED)modeling,the interval is used to describe prediction uncertainties.An ED model with interval uncertainty is established in this paper.The probability degree definition is adopted to convert the interval-based economic dispatch model into a deterministic model for the purposes of solving the modeling problem.Simulation tests are performed on a 10-machine system using professional optimization software(LINGO).The simulation results verify the validity of the proposed interval-based scheme for the economic dispatch of a power system with VPP.

Gauss optimization method for the dynamics of unilateral contact of rigid multibody systems

The discontinuous dynamical problem of multi-point contact and collision in multi-body system has always been a hot and difficult issue in this field.Based on the Gauss’principle of least constraint,a unified optimization model for multibody system dynamics with multi-point contact and collision is established.The paper presents the study of the numerical solution scheme,in which particle swarm optimization method is used to deal with the corresponding optimization model.The article also presents the comparison of the Gauss optimization method(GOM)and the hybrid linear complementarity method(i.e.combining differential algebraic equations(DAEs)and linear complementarity problems(LCP)),commonly used to solve the dynamic contact problem of multibody systems with bilateral constraints.The results illustrate that,the GOM has the same advantage of dynamical modelling with LCP and when the redundant constraint exists,the GOM always has a unique solution and so no additional processing is needed,whereas the corresponding DAE-LCP method may have singular cases with multiple solutions or no solutions.Using numerical examples,the GOM is verified to effectively solve the dynamics of multibody systems with redundant unilateral and bilateral constraints without additional redundancy processing.The GOM can also be applied to the optimal control of systems in the future and combined with the parameter optimization of systems to handle dynamic problems.The work given provides the dynamics and control of the complex system with a new train of thought and method.

Review of Power Module Automatic Layout Optimization Methods in Electric Vehicle Applications

The layout of power modules is a crucial design consideration,especially for silicon carbide devices.For electrical layout,optimizing the parasitic parameters can improving switching loss and dynamic behavior.For thermal layout,reducing the thermal resistance and controlling thermal capacitance can reduce the local hot point.Conventional layout design iterations are based on human knowledge and experience.But the major drawback of manual design methods is a limited choice of candidates,large time consumption and also the lack of consistency.With the introduce of automatic layout design,these challenges can be overcome which in the meanwhile alleviates current and temperature imbalance.By reviewing element representation,placement,routing,fitness evaluation,and the optimization algorithm approaches,a state-of-the-art power module layout design method for electric vehicle applications is introduced.

Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.

Network Structure Optimization Method for Urban Drainage Systems Considering Pipeline Redundancies

Redundancy is an important attribute of a resilient urban drainage system.While there is a lack of knowledge on where to increase redundancy and its contribution to resilience,this study developed a framework for the optimal network structure of urban drainage systems that considers pipeline redundancies.Graph theory and adaptive genetic algorithms were used to obtain the initial layout and design of the urban drainage system.The introduction of additional water paths(in loop)/redundancies is suggested by the results of complex network analysis to increase resilience.The drainage performances of the urban drainage system with pipeline redundancies,and without redundancies,were compared.The proposed method was applied to the study area in Dongying City,Shandong Province,China.The results show that the total overflow volume of the urban drainage system with pipeline redundancies under rainfall exceeding the design standard(5 years) is reduced by 20-30%,which is substantially better than the network without pipeline redundancies.

Optimization Methods for Box-Constrained Nonlinear Programming Problems Based on Linear Transformation and Lagrange Interpolating Polynomials

In this paper,an optimality condition for nonlinear programming problems with box constraints is given by using linear transformation and Lagrange interpolating polynomials.Based on this condition,two new local optimization methods are developed.The solution points obtained by the new local optimization methods can improve the Karush–Kuhn–Tucker(KKT)points in general.Two global optimization methods then are proposed by combining the two new local optimization methods with a filled function method.Some numerical examples are reported to show the effectiveness of the proposed methods.

Research on the Optimization of a Drilling Rock Breaking Method Based on Fuzzy Cluster Analysis

Improving drilling efficiency is the best way to reduce drilling costs and the choice of the drilling mode is instrumental in doing so.At present,however,a standard approach for the optimization of these processes does not exists yet.Through a comparative statistical analysis of the rock-breaking mechanisms and the characteristics of different drilling methods,this research proposes a set of cues to achieve this objective.Available statistical data are classified by means of a fuzzy cluster analysis according to the anti-drilling characteristic parameters of formation.The results show that different drilling methods rely on their own rock breaking mechanisms and have distinct characteristics.The rotary table drilling method is the most commonly used drilling mode,however,it displays some limitations with regard to deep wells,ultra-deep wells and difficult formations.The combined drilling method has the advantages of both the rotary table drilling and the down-hole power drilling modes.Polycrystalline diamond compact(PDC)drill bits can lead to good results for medium hardness and weakly abrasive formations.Underbalanced drilling for formations with high hardness and strong abrasiveness displays some limitations.

An Optimization Method in Inverse Elastic Scattering for One-Dimensional Grating Profiles

Consider the inverse diffraction problem to determine a two-dimensional periodic structure from scattered elastic waves measured above the structure.We formulate the inverse problem as a least squares optimization problem,following the two-step algorithm by G.Bruckner and J.Elschner[Inverse Probl.,19(2003),315–329]for electromagnetic diffraction gratings.Such a method is based on the Kirsch-Kress optimization scheme and consists of two parts:a linear severely ill-posed problem and a nonlinear well-posed one.We apply this method to both smooth(C2)and piecewise linear gratings for the Dirichlet boundary value problem of the Navier equation.Numerical reconstructions from exact and noisy data illustrate the feasibility of the method.

Сomputational Experience Optimization of Colors in Complex Fractal Images in Carpet Design Using the Simplex Method

This article proposes a new approach based on linear programming optimization to solve the problem of determining the color of a complex fractal carpet pattern.The principle is aimed at finding suitable dyes for mixing and their exact concentrations,which,when applied correctly,gives the desired color.The objective function and all constraints of the model are expressed linearly according to the solution variables.Carpet design has become an emerging technological field known for its creativity,science and technology.Many carpet design concepts have been analyzed in terms of color,contrast,brightness,as well as other mathematical concepts such as geometric changes and formulas.These concepts represent a common process in the carpet industry.This article discusses the use of complex fractal images in carpet design and simplex optimization in color selection.

Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition

Freezing of Gait(FOG)is the most common and disabling gait disorder in patients with Parkinson’s Disease(PD),which seriously affects the life quality and social function of patients.This paper proposes a FOG recognition method based on the Variational Mode Decomposition(VMD).Firstly,VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal.Secondly,to improve the accuracy and speed of the recognition algorithm,use the CART model as the base classifier and perform the feature dimension reduction.Then use the RUSBoost ensemble algorithm to solve the problem of unbalanced sample size and considerable limitations of a single classifier.Finally,the hyperparam-eters of the ensemble classifier are optimized by Bayesian optimization,and the experiment proves that the RUSBoost algorithm can complete the gait recognition task well.Compared with the Adaboost,Tomeklinks-Adaboost and ROS-Adaboost ensemble algorithms,the RUSBoost ensemble algorithm can complete the FOG recognition task more efficiently.When the maximum number of splits is 1023,and the number of base classifiers is 100,the performance of the RUSBoost ensemble algorithm can reach the best.The accuracy of the time recognition algorithm was 87.8%,the sensitivity was 89.7%,and the specificity was 87.5%.

Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

For heating systems based on electricity storage coupled with solar energy and an air source heat pump(ECSA),choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency.In this paper,four cities in three climatic regions in China were selected,namely Nanjing in the hot summer and cold winter region,Tianjin in the cold region,Shenyang and Harbin in the severe cold winter region.The levelized cost of heat(LCOH)was used as the economic evaluation index,and the energy consumption and emissions of different pollutants were analyzed.TRNSYS software was used to simulate and analyze the system performance.The Hooke-Jeeves optimization algorithm and GenOpt software were used to optimize the system parameters.The results showed that ECSA systemhad an excellent operation effect in cold region and hot summer and cold winter region.Compared with ECS system,the systemenergy consumption,and the emission of different pollutants of ECSA system can be reduced by a maximum of 1.37 times.In cold region,the initial investment in an air source heat pump is higher due to the lower ambient temperature,resulting in an increase in the LOCH value of ECSA system.After the LOCH value of ECSA system in each region was optimized,the heating cost of the system was reduced,but also resulted in an increase in energy consumption and the emission of different pollutant gases.