Hybrid Global Optimization Method Research Articles

Globally Optimal Algorithm of Superconducting Magnet Design for Gyrotrons

This paper presentsa new hybrid global optimization method to design a 25 T All YBCO solenoid superconducting magnet with a warm-bore diameter of 52 mm for high-frequency gyrotrons in Wuhan National High Magnetic Field Center, China. This method includes 3 main routines: linear programming (LP) code to get the non-rectangular coil design, coil-picking code to get rectangular coil design, and nonlinear programming (NLP) code to get final integer coil design by using rectangular coil design as initial input. The designed magnet includes Inner, Middle and Outer coils which consist of 34, 56 and 68 double pancakes (DP) wound by 4 mm x 0.1 mm bare YBCO tape, respectively. The designed peak-peak field in-homogeneity is 50 ppm within 10 mm Diameter of Spherical Volume (DSV). The designed 5 Gauss line is restricted in a cylinder with the sizes of 10 m in axial direction and 8 m in radial direction, respectively. The maximum hoop stress is limited below 450 MPa. Layer-layer separation happens in the inner coil since non-insulation (NI) winding is adopted. The results show that this methodology is very efficient for High-frequency Gyrotron superconducting magnet design. This methodology also can be used for other solenoid superconducting magnet design, such as Magnetic Resonance Imaging (MRI) superconducting magnet and Nuclear Magnetic Resonance (NMR) superconducting magnet.

GNSS-R Soil Moisture Retrieval for Flat Vegetated Surfaces Using a Physics-Based Bistatic Scattering Model and Hybrid Global/Local Optimization

This paper presents a soil moisture retrieval scheme from Cyclone Global Navigation Satellite System (CYGNSS) delay-Doppler maps (DDMs) over land. The proposed inversion method consists of a hybrid global and local optimization method and a physics-based bistatic scattering forward model. The forward model was developed for bare-to-densely vegetated terrains, and it predicts the circularly polarized bistatic radar cross section DDM of the land surface. This method was tested on both simulated DDMs and CYGNSS DDMs over the Soil Moisture Active Passive (SMAP) Yanco core validation site in Australia. About 250 CYGNSS DDMs from 2019 and 2020 over the Yanco site were used for validation. The simulated DDMs were for grassland and forest vegetation types. The vegetation type of the Yanco validation site was grassland. The vegetation water content (VWC) was 0.19 kgm−2 and 4.89 kgm−2 for the grassland and forest terrains, respectively. For the case when the surface roughness is known to the algorithm, the unbiased root mean square error (ubRMSE) of soil moisture estimates was less than 0.03 m3m−3 while it was approximately 0.06 m3m−3 and 0.09 m3m−3 for the validation results from 2019 and 2020, respectively. The retrieval algorithm generally had enhanced performance for smaller values of soil moisture. For the case when both the soil moisture and surface roughness are unknown to the algorithm and only a single DDM is used for retrieval, the validation results showed an expected reduced performance, with an an ubRMSE of less than 0.12 m3m−3.

Redesign of casing treatment for a transonic centrifugal compressor based on a hybrid global optimization method

As a typical black-box problem, recirculating casing treatment (RCT) optimization of compressor stages is computationally intensive and time consuming, even though surrogate models are usually employed. In order to improve efficiency and robustness of the optimization, an expected-improvement (EI) based hybrid global optimization (EHGO) algorithm is developed by coupling an EI-based surrogate model with a hybrid optimization algorithm. Highly nonlinear and multiple modality mathematical tests show that the EHGO algorithm is able to create a high-fidelity surrogate model near targeted regions with less evaluated samples, and to obtain the global optimal solution simultaneously. The RCT of a compressor stage is optimized based on this algorithm. The number of CFD simulations required for obtaining an optimum solution is greatly reduced, as compared to similar studies using conventional methods. The optimization results show that the aerodynamic performance is improved over the whole speed line and the flow range is also extended. The dominant factors for the performance improvements and the enhanced stall margin are addressed by analyzing the local flow characteristics before and after optimization. It is found that those factors include: removing a larger amount of low-momentum fluid, achieving a more uniform flow of impeller passage in circumferential direction, and reducing the radial distortion of impeller inlet flow. The proposed algorithm has the potential to considerably speed up the optimization process and make the optimization much more accessible. It can be generalized to deal with other computationally intensive black-box problems, for example, turbomachinery optimization.

Hybrid Global Optimization Method Based on Dynamic Kriging Metamodel and Gradient Projection Method for Optimal Design of Robot

Hybrid Global Optimization Method Based on Dynamic Kriging Metamodel and Gradient Projection Method for Optimal Design of Robot

A hybrid global optimization method based on multiple metamodels

PurposeThis paper aims to propose hybrid global optimization based on multiple metamodels for improving the efficiency of global optimization.Design/methodology/approachThe method has fully utilized the information provided by different metamodels in the optimization process. It not only imparts the expected improvement criterion of kriging into other metamodels but also intelligently selects appropriate metamodeling techniques to guide the search direction, thus making the search process very efficient. Besides, the corresponding local search strategies are also put forward to further improve the optimizing efficiency.FindingsTo validate the method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the global optimization efficiency of the proposed method is higher than that of the other methods for most situations.Originality/valueThe proposed method sufficiently utilizes multiple metamodels in the optimizing process. Thus, good optimizing results are obtained, showing great applicability in engineering design optimization problems which involve costly simulations.

A hybrid approach to constrained global optimization

In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to.

A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box

<p class="zhengwen">This paper proposes a hybrid genetic algorithm method for optimizing constrained black box functions utilizing shrinking box and exterior penalty function methods (SBPGA). The constraints of the problem were incorporated in the fitness function of the genetic algorithm through the penalty function. The hybrid method used the proposed Variance-based crossover (VBC) and Arithmetic-based mutation (ABM) operators; moreover, immigration operator was also used. The box constraints constituted a hyperrectangle that kept shrinking adaptively in the light of the revealed information from the genetic algorithm about the optimal solution. The performance of the proposed algorithm was assessed using 11 problems which are used as benchmark problems in constrained optimization literatures. ANOVA along with a success rate performance index were used to analyze the model.</p>Based on the results, we believe that the proposed method is fairly robust and efficient global optimization method for Constrained Optimization Problems whether they are continuous or discrete.

Estimation of Archie parameters by a novel hybrid optimization algorithm

Abstract Archie parameters play a critical role in accurately identifying water saturation for a given reservoir condition. Due to interdependence of these parameters, it is difficult to estimate them accurately. In order to achieve more accurate parameters, we model a non-convex optimization problem based on the core Archie parameters’ estimate (CAPE). Then we present a new hybrid global optimization method to solve this non-convex problem. The hybrid technique has the features of both fast local convergence in interior point method and global convergence in Firefly algorithm (FA). Finally, our method was implemented to determine Archie parameters and some comparisons are done among two deterministic techniques and four population-based algorithms. Water saturation profiles were generated using the different Archie parameters determined by six techniques. These profiles have shown a significant difference in water saturation values between CAPE methods and population-based methods. These results highlight that our proposed algorithm performed better than conventional CAPE and three dimension (3D) method for reservoirs to calculate the water saturation values due to more accurate Archie parameters achieved by our method.

Short-term multi-objective optimization scheduling for cascaded hydroelectric plants with dynamic generation flow limit based on EMA and DEA

A dynamic generation flow strategy using for dynamically coordinating water head of reservoir and water consumption volume for electric energy production is presented in this paper. A novel multi-objective scheduling model is proposed to achieve the optimal trade-off between water volume for generation and electric quantity. To realize the optimal power output the coordination condition is used to describe the relationship between the current water head level and generation flow rate. The constraints are presented in an efficient method based on characters of the cascaded hydroelectric plants. A hybrid global optimization method, which can overcome the shortcoming of weighted method, is proposed for solving the multi-objective problem efficiently. This is done by embedding the data envelopment analysis (DEA) into an electromagnetism-like algorithm (EMA). A test system with eight hydroelectric plants was used to verify this new method. Results show that this novel scheduling method can enhance the synthesis generation benefit of the cascaded hydroelectric plants and realize the optimal solution for the scheduling model.

An efficient, accurate approach to medial axis transforms of pockets with closed free-form boundaries

Medial axis transform of a pocket with free-form closed boundaries is a completed, compact representation of the pocket geometric shape and topology. It is very useful to multiple cutters selection and their tool paths generation for CNC machining of complex pockets. In the past decades, much research has been successfully conducted on the topic of finding the medial axis of a shape domain bounded with a polygon or simple geometries, e.g., lines and circles. Currently, more pockets with free-form boundaries are adopted in mechanical parts; however, the prior medial axis generation methods cannot handle this type of pockets well, resulting in long computation time and low medial axis accuracy. To address this problem, an efficient, accurate approach to calculating the medial axis transforms of these pockets is proposed in this work. An original optimization model of bisectors is established, and a new optimization method—the hybrid global optimization method—is developed to efficiently and accurately solve the optimization model of bisectors. The new optimization model and solver have been applied to many examples, and the testing results have demonstrated the advantages of this innovative approach over the prior medial axis methods. It can be an effective solution to the medial axis transforms of complex pockets.

A hybrid global optimization method based on artificial immune system

A hybrid global optimization method based on artificial immune system

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques

BackgroundSystems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques.ResultsHere, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model.ConclusionsIn the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

A hybrid global optimization algorithm and its application to parameter estimation problems

AbstractGlobal optimization has been an area of active research for the past few decades. There are many different global optimization algorithms that have been proposed and studied. Among these, differential evolution (DE) is a promising population‐based stochastic global optimization algorithm. It has been gaining popularity in the recent past due to its capability to handle non‐convex, non‐differentiable and multimodal objective functions. In this work, a hybrid global optimization (HGO) method with a novel stopping criterion is proposed. The proposed algorithm integrates DE with a taboo list of taboo search and self‐adaptive strategies. The taboo list/check prevents revisiting the same area, thus increasing the population diversity and computational efficiency. The self‐adaptive strategy reduces the algorithm parameters to be provided and makes the algorithm more robust. Furthermore, a novel stopping criterion based on the number of rejected points is developed. The effectiveness of the proposed stopping criterion and HGO is assessed on more than 30 benchmark problems with 2–30 variables. The performance of HGO is compared with some state‐of‐the‐art global optimization algorithms recently published in the literature. The results show that the reliability of finding the global optimum by HGO is slightly better than that by the latest DE techniques in the literature, and the number of function evaluations (NFE) of HGO is significantly reduced. Finally, HGO is tested for parameter estimation in dynamic models. In conclusion, the proposed HGO algorithm with the novel stopping criterion is found to be reliable, efficient and suitable for application problems. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.

An improved hybrid global optimization method for protein tertiary structure prediction

First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.

Intelligent Visual Recognition and Classification of Cork Tiles With Neural Networks

An intelligent machine vision system is investigated and used for pattern recognition and classification of seven different types of cork tiles. The system includes image acquisition with a charge-coupled device (CCD) camera, texture feature generation (co-occurrence matrices and Laws' masks), analysis and processing of the feature vectors [linear discriminant analysis (LDA) and principal component analysis (PCA)], and cork tiles classification with feedforward neural networks (NN), employing our GLP(tau) S (genetic low-discrepancy search) hybrid global optimization method. In addition, the same NN are trained with backpropagation (BP) and the obtained results are compared with the ones from GLP(tau) S . The NN generalization abilities are discussed and assessed with respect to the NN architectures and the texture feature sets. The reported results are very encouraging with testing rate reaching up to 95%.

Parameter identification theory of a complex model based on global optimization method

With the development of computer technology and numerical simulation technology, computer aided engineering (CAE) technology has been widely applied to many fields. One of the main obstacles, which hinder the further application of CAE technology, is how to successfully identify the parameters of the selected model. An elementary framework for parameter identification of a complex model is provided in this paper. The framework includes the construction of objective function, the design of the optimization method and the evaluation of the identified results, etc. The parameter identification process is described in this framework, taking the parameter identification of the superplastic constitutive model considering grain growth for Ti-6Al-4V at 927°C as an example. The objective function is the weighted quadratic sums of the difference between the experimental and computational data for the stress-strain relationship and the grain growth relationship; the designed optimization method is a hybrid global optimization method, which is based on the feature of the objective function and incorporates the strengths of genetic algorithm (GA), the Levenberg-Marquardt algorithm and the augmented Gauss-Newton algorithm. The reliability evaluation of parameter identification result is made through the comparison between the calculated and experimental results and between the theoretical values of the parameters and the identified ones.

DESA: a new hybrid global optimization method and its application to analog integrated circuit sizing

This paper presents a new hybrid global optimization method referred to as DESA. The algorithm exploits random sampling and the metropolis criterion from simulated annealing to perform global search. The population of points and efficient search strategy of differential evolution are used to speed up the convergence. The algorithm is easy to implement and has only a few parameters. The theoretical global convergence is established for the hybrid method. Numerical experiments on 23 mathematical test functions have shown promising results. The method was also integrated into SPICE OPUS circuit simulator to evaluate its practical applicability in the area of analog integrated circuit sizing. Comparison was made with basic simulated annealing, differential evolution, and a multistart version of the constrained simplex method. The latter was already a part of SPICE OPUS and produced good results in past research.

Parameter Identification of Thermal Visco-Plastic Model Considering Dynamic Recrystallization

A hybrid global optimization method combining the Real-coded genetic algorithm and some classical local optimization methods is constructed and applied to develop a special program for parameter identification. Finally, the parameter identification for both 26Cr2Ni4MoV steel and AZ31D magnesium alloy is carried out by using the program. A comparison of deformation test and numerical simulation shows that the parameter identification and the obtained two sets of material parameters are all available.

Optimal Design of Magnitude Responses of Rational Infinite Impulse Response Filters

This correspondence considers a design of magnitude responses of optimal rational infinite impulse response (IIR) filters. The design problem is formulated as an optimization problem in which a total weighted absolute error in the passband and stopband of the filters (the error function reflects a ripple square magnitude) is minimized subject to the specification on this weighted absolute error function defined in the corresponding passband and stopband, as well as the stability condition. Since the cost function is nonsmooth and nonconvex, while the constraints are continuous, this kind of optimization problem is a nonsmooth nonconvex continuous functional constrained problem. To address this issue, our previous proposed constraint transcription method is applied to transform the continuous functional constraints to equality constraints. Then the nonsmooth problem is approximated by a sequence of smooth problems and solved via a hybrid global optimization method. The solutions obtained from these smooth problems converge to the global optimal solution of the original optimization problem. Hence, small transition bandwidth filters can be obtained

Parallel hybrid algorithm for global optimization of problems occurring in MDS-based visualization

Multidimensional scaling is a widely used technique for visualization of multidimensional data. For the implementation of a multidimensional scaling technique a difficult global optimization problem should be solved. To attack such problems a hybrid global optimization method is developed combining evolutionary global search with local descent. A parallel version of the proposed method is implemented to enable solution of large scale problems in acceptable time. The results of the experimental investigation of the efficiency of the proposed method are presented.