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.

2D multi-scale hybrid optimization method for geophysical inversion and its application

Local and global optimization methods are widely used in geophysical inversion but each has its own advantages and disadvantages. The combination of the two methods will make it possible to overcome their weaknesses. Based on the simulated annealing genetic algorithm （SAGA） and the simplex algorithm, an efficient and robust 2-D nonlinear method for seismic travel-time inversion is presented in this paper. First we do a global search over a large range by SAGA and then do a rapid local search using the simplex method. A multi-scale tomography method is adopted in order to reduce non-uniqueness. The velocity field is divided into different spatial scales and velocities at the grid nodes are taken as unknown parameters. The model is parameterized by a bi-cubic spline function. The finite-difference method is used to solve the forward problem while the hybrid method combining multi-scale SAGA and simplex algorithms is applied to the inverse problem. The algorithm has been applied to a numerical test and a travel-time perturbation test using an anomalous low-velocity body. For a practical example, it is used in the study of upper crustal velocity structure of the A＇nyemaqen suture zone at the north-east edge of the Qinghai-Tibet Plateau. The model test and practical application both prove that the method is effective and robust.

PARETO FRONT CAPTURE USING DETERMINISTIC OPTIMIZATION METHODS IN MULTI-CRITERION AERODYNAMIC DESIGN

Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.

A NEW DERIVATIVE FREE OPTIMIZATION METHOD BASED ON CONIC INTERPOLATION MODEL

In this paper, a new derivative free trust region method is developed based on the conic interpolation model for the unconstrained optimization. The conic interpolation model is built by means of the quadratic model function, the collinear scaling formula, quadratic approximation and interpolation. All the parameters in this model are determined by objective function interpolation condition. A new derivative free method is developed based upon this model and the global convergence of this new method is proved without any information on gradient.

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.

Inverse displacement analysis of the general six degree-of-freedom serial robot based on optimization method

The paper presents a new solution of inverse displacement analysis of the general six degree-of-freedom serial robot.The inverse displacement analysis of the general serial robot is transformed into a minimization problem and then the optimization method is adopted to solve the nonlinear least squares problem with the analytic form of new Jacobian matrix.In this way,joint variables of the general serial robot can be searched out quickly under the desired precision when positions of the three non-collinear end effector points are given.Compared with the general Newton iterative method,the proposed algorithm can search out the solution when the robot is at the singular configuration and the initial configuration used in the optimization method may also be the singular configuration.So the convergence domain is bigger than that of the general Newton iterative method.Another advantage of the proposed algorithm is that positions of the three non-collinear end effector points are usually much easier to be measured than the orientation of the end effector.The inverse displacement analysis of the general 6R(six-revolute-joint) serial robot is illustrated as an example and the simulation results verify the efficiency of the proposed algorithm.Because the three non-collinear points can be selected at random,the method can be applied to any other types of serial robots.

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.

Design of small-scale gradient coils in magnetic resonance imaging by using the topology optimization method

A topology optimization method based on the solid isotropic material with penalization interpolation scheme is utilized for designing gradient coils for use in magnetic resonance microscopy.Unlike the popular stream function method,the proposed method has design variables that are the distribution of conductive material.A voltage-driven transverse gradient coil is proposed to be used as micro-scale magnetic resonance imaging(MRI)gradient coils,thus avoiding introducing a coil-winding pattern and simplifying the coil configuration.The proposed method avoids post-processing errors that occur when the continuous current density is approximated by discrete wires in the stream function approach.The feasibility and accuracy of the method are verified through designing the z-gradient and y-gradient coils on a cylindrical surface.Numerical design results show that the proposed method can provide a new coil layout in a compact design space.

Optimization Method for Indoor Thermal Comfort Based on Interactive Numerical Calculation

In order to implement the optimal design of the indoor thermal comfort based on the numerical modeling method, the numerical calculation platform is combined seamlessly with the data-processing platform, and an interactive numerical calculation platform which includes the functions of numerical simulation and optimization is established. The artificial neural network （ANN） and the greedy strategy are introduced into the hill-climbing pattern heuristic search process, and the optimizing search direction can be predicted by using small samples; when searching along the direction using the greedy strategy, the optimal values can be quickly approached. Therefore, excessive external calling of the numerical modeling process can be avoided, and the optimization time is decreased obviously. The experimental results indicate that the satisfied output parameters of air conditioning can be quickly given out based on the interactive numerical calculation platform and the improved search method, and the optimization for indoor thermal comfort can be completed.

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.

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.

Preconcentration of rare earth elements from Iranian monazite ore by spiral separator using multi-response optimization method

The present work dealt with the preconcentration of rare earth elements in Saghand ore(Yazd province,Iran)which was achieved by Humphrey spiral using orthogonal optimization method after scrubbing the sample at 45%solid pulp density for 30 min.The pulp was diluted and was fed to a Humphrey spiral for upgrading.The process parameters considered were feed size,feed solids and feed rate,and Taguchi’s L9(34)orthogonal array(OA)was selected for optimization of the process.The results show that the feed rate and feed size were more significant than the other operation parameters of the process.It was also found that under optimal conditions,the concentrate grade of rare earth elements increased from2860 10 6to 6050 10 6and recovery reached to 58%.

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.

THREE-DIMENSIONAL TRAJECTORY OPTIMIZATION WITH DIRECT METHOD

The principle of direct method used in optimal control problem is introduced. Details of applying this method to flight trajectory generation are presented including calculation of velocity and controls histories. And capabilities of flight and propulsion systems are considered also. Combined with digital terrain map technique, the direct method is applied to the three dimensional trajectory optimization for low altitude penetration, and simplex algorithm is used to solve the parameters in optimization. For the small number of parameters, the trajectory can be optimized in real time on board.

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.

PARAMETER IDENTIFICATION BY OPTIMIZATION METHOD FOR A POLLUTION PROBLEM IN POROUS MEDIA

In the present work, we investigate the inverse problem of reconstructing the parameter of an integro-differential parabolic equation, which comes from pollution problems in porous media, when the final observation is given. We use the optimal control framework to establish both the existence and necessary condition of the minimizer for the cost func- tional. Furthermore, we prove the stability and the local uniqueness of the minimizer. Some numerical results will be presented and discussed.

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.