Simulated Annealing Method Research Articles

Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture

The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D VS from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D VS model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method’s effectiveness. Our synthetic dataset proved the diversity and rationality of the random VS model. The results of training and predicting showed higher accuracy and could speed the inversion process (only ~15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving VS models for complex near-surface structures.

Design of online education model based on migratory bird algorithm under the concept of sustainable development

Abstract The emergence of a new industrial form, online education, has opened up an efficient and convenient learning channel for people. In this paper, the basic process and algorithmic ideas of the migratory bird algorithm are investigated, and the optimization is carried out by using a simulated annealing method for its problem of finding the optimal global solution too slowly, adding the inner loop of SA at each generation of particle swarm update, and improving the fitness value function and update criterion of each particle in the particle swarm based on the outer loop. Then, the online education model of this paper is designed based on the SA-PSO algorithm. For the evaluation of interaction quality, this model obtains a comprehensive evaluation index of 0.856, which is 10.24% higher than New Oriental and 18.65% higher than NetEase Cloud Classroom. In terms of service quality, the overall evaluation index of this model is 8.73% higher than that of New Oriental and 20.35% higher than NetEase Cloud Classrooms. In terms of system quality, the overall evaluation index of this model is 4.31% higher than that of New Oriental and 18.69% higher than NetEase Cloud Classrooms. This study proposes rationalized improvement strategies for the limitations of the existing online education model, and the model based on the SA-PSO algorithm helps to promote the quality of online education.

Sliding Mode Controller Based on Genetic Algorithm and Simulated Annealing for Assured Crew Reentry Vehicle

A sliding mode (SM) re-entry control system is proposed for an assured crew re-entry vehicle (ACRV). The vehicle has a low lift/drag ratio (0.3). The trajectory controller drives the ACRV in a predefined track. The reference vectors for the attitude controller are generated by the trajectory controller and navigation system. The feedback SM controller is designed to accommodate the structural parametric uncertainties and environmental disturbances. The SM controller is optimized by both the genetic algorithm (GA) and the simulated annealing (SA) methods. A comparative study is done between both optimization methods and the efficiency of the SA method is proved. The system is subjected to a sensitivity study in terms of various thrust torque profiles. A robustness check is done by changing the system parameters. A pulse width pulse frequency (PWPF) modulator modulates the output torque dictated by the attitude controller. The simulation results demonstrate the proposed method’s effectiveness in achieving the various required parameters.

Integrated scheduling problems of open shop and vehicle routing using an ensemble of group teaching optimization and simulated annealing

Production and distribution are two parts of importance in supply chains. Yet, seldom attention is given to an integrated production and distribution in the case of open shop scheduling and vehicle routing problems. This article addresses an integrated scheduling problem of open shop and vehicle routing optimization, in which the jobs are processed in an open shop, and subsequently are delivered to customers at diverse places. First, a mixed integer programming model is developed to define the problem with minimizing maximum completion time. Second, an ensemble of group teaching optimization and simulated annealing methods is devised to optimally handle the considered problem. A heuristic rule is designed to generate a high-quality initial solution, while a simulated annealing method considering key machines is proposed to reinforce exploitation abilities. A nearest insertion method is used to make delivery decisions of vehicles. At last, the sensitivity analysis of user parameters in the developed approach is executed. Via solving a set of benchmark instances, we carry out extensive comparison experiments. The formulated model is verified by a mathematical programming optimizer CPLEX and the devised approach is compared with four meta-heuristics. The results and discussions confirm the strong competitiveness of the developed model and method for solving the concerned problems.

Petrophysical parameters inversion for heavy oil reservoir based on a laboratory-calibrated frequency-variant rock-physics model

Heavy oil has high density and viscosity, and exhibits viscoelasticity. Gassmann's theory is not suitable for materials saturated with viscoelastic fluids. Directly applying such model leads to unreliable results for seismic inversion of heavy oil reservoir. To describe the viscoelastic behavior of heavy oil, we modeled the elastic properties of heavy oil with varying viscosity and frequency using the Cole-Cole-Maxwell (CCM) model. Then, we used a CCoherent Potential Approximation (CPA) instead of the Gassmann equations to account for the fluid effect, by extending the single-phase fluid condition to two-phase fluid (heavy oil and water) condition, so that partial saturation of heavy oil can be considered. This rock physics model establishes the relationship between the elastic modulus of reservoir rock and viscosity, frequency and saturation. The viscosity of the heavy oil and the elastic moduli and porosity of typical reservoir rock samples were measured in laboratory, which were used for calibration of the rock physics model. The well-calibrated frequency-variant CPA model was applied to the prediction of the P- and S-wave velocities in the seismic frequency range (1–100 Hz) and the inversion of petrophysical parameters for a heavy oil reservoir. The pre-stack inversion results of elastic parameters are improved compared with those results using the CPA model in the sonic logging frequency (∼10 kHz), or conventional rock physics model such as the Xu-Payne model. In addition, the inversion of the porosity of the reservoir was conducted with the simulated annealing method, and the result fits reasonably well with the logging curve and depicts the location of the heavy oil reservoir on the time slice. The application of the laboratory-calibrated CPA model provides better results with the velocity dispersion correction, suggesting the important role of accurate frequency dependent rock physics models in the seismic prediction of heavy oil reservoirs.

A Network-on-Chip-Based Annealing Processing Architecture for Large-Scale Fully Connected Ising Model

Combinatorial optimization problems are prevalent in many different fields. Most of these problems are NP-hard and challenging for computers with conventional Von-Neumann architecture. Ising machines with a number of spins have the potential to solve these problems by emulating the natural annealing process of solid matter. Recent research has explored some hardware implementation methods of Ising machines to accelerate the convergence process of such problems at room temperature. However, most of them are suffering from low scalability and low parallel processing capability due to the huge hardware cost and high complexity. In this paper, a novel network-on-chip-based annealing processing architecture (NoCAPA) for a large-scale Ising processor is described to address these issues with a NoC computing paradigm, a distributed storage scheme, and a fully pipelined structure design. Several techniques are developed to further increase convergence speed and reduce hardware resource consumption, including a dynamic multithread parallel update algorithm, a router with merge and deflection abilities, and a unique multiply-accumulate operation. The prototype is implemented in FPGA with the maximum operation frequency of 200MHz, achieving up to 120.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> faster than conventional simulated annealing method when solving the max-cut problem while supporting high scalability.

3D hybrid stochastic reconstruction of catalyst layers in proton exchange membrane fuel cells from 2D images

Proton exchange membrane fuel cells show promise in a variety of applications, aiding global efforts to achieve net-zero emissions. Its catalyst layer is a source of significant efficiency loss due to resistance to transport and reaction mechanisms in its porous structure. A computational reconstruction model of the porous microstructure is required to study the applicable pore-scale phenomena in the catalyst layer. A hybrid stochastic reconstruction technique is presented which reconstructs a catalyst layer from 2D scanning electron microscope experimental images, combining a sphere-packing approach with sphere-based simulated annealing and simultaneously optimizing the microstructure using the two-point correlation and the lineal path distribution. This technique (Method 2) is compared to a conventional sphere-based simulated annealing method (Method 1) and to a fabrication-based reconstruction (Method 0). Pore size distributions, surface area, porosity, mean pore diameter and mean chord lengths are used to validate the model and evaluate the accuracy of each reconstruction method. Both Method 1 and 2 result in porosity values within approximately 0.0001% of the reference porosity. Method 2 achieves higher accuracy than Methods 1 and 0 in the mean chord lengths and surface area, and both Methods 1 and 2 improve in the mean pore size over Method 0.

Parameter extraction of photovoltaic module model by using Levenberg-Marquardt algorithm based on simulated annealing method

Reliable and accurate parameter identification of solar modules model is necessary to evaluate the performances and to control the behavior of photovoltaic systems. In this work, Levenberg-Marquardt combined with simulated annealing algorithm is proposed to extract the five parameters from the experimental data points ( $$I_\text {{PV}}$$ - $$V_\text {{PV}}$$ ) of Photowatt-PWP 201 polycrystalline module. The quality evaluation of the obtained outcomes is ensured by the analysis of the objective function accuracy. Therefore, the proposed method provides the highest precision compared to the results of the most recent methods reported in the literature. Moreover, a study of the convergence process has been established in the aim to observe the adjustment way of the five parameters and to assess the influence of the damping factor on the objective function optimization. The superiority of the proposed method in terms of accuracy have been tested and proven by using the $$I_\text {{PV}}$$ ( $$V_\text {{PV}}$$ ) measurements of SM55 mono-crystalline photovoltaic module.

Method of Site Selection and Capacity Setting for Battery Energy Storage System in Distribution Networks with Renewable Energy Sources

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method.

Non‐spherical shape optimization of artificial hip joint based on elastohydrodynamic lubrication analysis

AbstractStructural optimization design is a promising way to further promote the lubrication performance of the artificial hip joint (AHJ). In this work, a non‐spherical shape optimization design strategy for the femoral head is firstly proposed. The generatrix, the line rotated about an axis that generates the bearing surface of the femoral head, is characterized based on a third‐order Bezier curve. The performance of the AHJ under steady‐state condition is analyzed by solving the governing equations of the elastohydrodynamic lubrication problem, and the minimum lubricating film thickness of the AHJ is maximized by adjusting the positions of four control points of the Bezier curve based on the simulated annealing method. The results show that, when compared with the standard spherical femoral head, the optimized non‐spherical femoral head can significantly improve the minimum thickness and reduce the maximum pressure of the lubricating film of the AHJ. Through the analysis of the optimization process, the results further reveal that the shape of the femoral head has a direct impact on the thickness distribution of the lubricating film. This research will provide guidance and theoretical support for novel design of AHJs.

Hybrid Quantum Annealing for Larger-than-QPU Lattice-structured Problems

Quantum processing units (QPUs) executing annealing algorithms have shown promise in optimization and simulation applications. Hybrid algorithms are a natural bridge to larger applications. We present a simple greedy method for solving larger-than-QPU lattice-structured Ising optimization problems. The method, implemented in the open source D-Wave Hybrid framework, uses a QPU coprocessor operating with generic parameters. Performance is evaluated for standard spin-glass problems on two lattice types with up to 11,616 spin variables, double the size that is directly programmable on any available QPU. The proposed method is shown to converge to low-energy solutions faster than an open source simulated annealing method that is either directly employed or substituted as a coprocessor in the hybrid method. Using newer Advantage QPUs in place of D-Wave 2000Q QPUs is shown to enhance convergence of the hybrid method to low energies and to achieve a lower final energy.

Pore-Scale Simulation of Effective Transport Coefficients in the Catalyst Layer of Proton Exchange Membrane Fuel Cells

The catalyst layer (CL) of the proton exchange membrane (PEM) fuel cells is reconstructed using the sphere-based simulated annealing (SA) method. By changing carbon phase volume fraction and ionomer loading, the transports of oxygen, water vapor and proton inside the reconstructed CLs are studied using the D3Q7 multiple-relaxation-time (MRT) lattice Boltzmann (LB) method. The results show that Knudsen diffusion has an important role in gas diffusion in CL. The carbon phase distribution influences the gas diffusion process but has little effect on proton conduction. The simulated effective gas diffusivities in CL can fit the experimental data very well if using directly measured porosities, and a fitting formula is proposed to predict the effective diffusivity with porosity. Besides, the simulated effective proton conductivities in CL are in reasonable agreement with the experimental data if considering the ionomer swelling and proton conduction in the condensed water. The influence of the structural randomness of CL during the reconstruction process on the pore-scale simulations is also analyzed. The results show that the relative errors caused by structure randomness for the same structural parameters are not obvious. Therefore, a small number of reconstruction samples are enough to simulate the reasonable transport parameters of CL.

Detecting impersonation episode using teaching learning‐based optimization and support vector machine techniques

AbstractOver the last few decades, computer and internet security has become a vital area because of eye‐opening numbers of data breaches, intruders on perilous infrastructure and malware attacks that are increasing day by day. In order to monitor abnormal activities and identify unusual attacks, several security solutions have been proposed in the recent past years. To protect computer system, intrusion detection system (IDS) opens up great opportunities to determine vulnerabilities and detect anomalies in security system. For detecting new attacks or building security solutions, in this study we present a new wrapper technique for security specialists that can help to detect more complicated attacks by combining teaching learning‐based optimization with opposition learning scheme and simulated annealing method. In order to address advance attack, firstly opposition learning strategy is used to update population generation of teaching learning‐based optimization that affect the robustness of the model after that simulated annealing is integrated into the teaching learning‐based optimization. In proposed method to choose the relevant features, we have used support vector machine as a fitness function that can be helped to recognize attacks precisely. The proposed algorithm is evaluated on three popular datasets namely NSL‐KDD, ISCX 2012 and UNSW‐NB15. Experimental results show that the proposed method is superior to other existing wrapper algorithms in terms of detection rate, accuracy and false alarm rates.

Machine-learning accelerated annealing with fitting-search style for multicomponent alloy structure predictions

Structural prediction for the discovery of novel materials is a long sought-after goal of computational physics and materials sciences. The success is rather limited for methods such as the simulated annealing method (SA) that require expensive density functional theory (DFT) calculations and follow unintelligent search paths. Here a machine-learning based crystal combinatorial optimization program (CCOP) with a fitting-search style is proposed to drastically improve the efficiency of structural search in SA. CCOP uses a graph neural network energy prediction model to reduce the DFT cost and a deep reinforcement learning algorithm to direct the search path. Tests on six multicomponent alloys show the energy prediction model is capable of extracting the bonding characteristics of the complex alloys to achieve interpretability. It also achieves high accuracy with a tiny training set (an increment of 30 samples per iteration) by active learning in less than five iterations. Comparison with a few conventional methods shows that CCOP finds the lowest-energy structures with the smallest number of search steps. CCOP cuts the computing cost of SA by two orders of magnitude, while providing better search results than SA. CCOP is promising for serving as a broadly applicable tool for the efficient crystal structure predictions.

Application of Simulated Annealing Method on Tabarru-Fund Valuation using Inflator by Vasicek Model Approach Based on Profit and Loss Sharing Scheme

Currently, the financial services industry is dominated by conventional banks and individuals that apply the system of interest or an excess of loans. In Islam, this excess is referred to as usury, which is prohibited by Islamic law because, in practice, usury makes borrowers poorer as they cannot pay such high-interest installments. Not to mention, late payments are subject to penalties that will continue to accumulate if the borrower is unable to pay the next installment. From these facts, this system is prohibited by Islamic Law because there are harmed parties. Therefore, this research discusses mathematical models in the form of Islamic investment business loans for micro-economic traders by implementing a profit and loss sharing system. Tabarru-fund is a set of funds derived from borrowers’ contributions used to overcome conditions when they experience losses in certain conditions. In this mathematical model, the tabarru-fund acts as the premium that must be paid if the borrower is still profitable after the principal installments have paid off. This sharia model with tabarru funds is obtained by calculating the premium which involves the problem of minimizing the remaining tabarru funds in a certain period. The future value of the trader's profit rate will be projected using the Vasicek Model approach which previously determined the parameter estimation using OLS regression and then the data is generated using Monte Carlo simulation so that the sharia inflator is obtained. This sharia inflator plays a role in the optimization process of minimizing the remaining tabarru-fund which will be solved by the Simulated Annealing (SA) algorithm.

A Comparison of Faulty Antenna Detection Methodologies in Planar Array

Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) as the objective function. The analysis was varied for 8 × 8 and 6 × 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element’s location in a planar array. The pattern search method produced outstanding results in finding the faulty element’s locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.

A new framework for the reconstruction of porous media based on statistical characteristics: Multiscale erosion simulated annealing method

In this paper, a multiscale erosion simulated annealing method (MESA) is proposed, which can improve the computational efficiency of microstructure reconstruction by an order of magnitude. The reconstruction is performed on two-dimensional (2D) and three-dimensional (3D) microstructure based on simulated annealing (SA) and MESA, where the two-point correlation function is selected as the morphological information descriptor. The two-point cluster function containing significant high-order statistical information is used to verify the reconstruction results. From the analysis of 2D reconstruction, it can be found that although the proposed MESA technology can improve the reconstruction efficiency, the reconstruction is still not perfect. That is because the two-point correlation function contains insufficient information. In the 3D reconstruction, the two-point correlation function of the reconstructed 3D microstructure has a good consistency with the original 2D two-point correlation function, indicating that our proposed method is effective for the 3D microstructure reconstruction. By comparing the energy and computation time of SA and MESA, it is shown that our proposed method is one order of magnitude faster than SA method. This is because only a small part of pixels in the overall scales need to be sampled.

QBoost for regression problems: solving partial differential equations

A hybrid algorithm based on machine learning and quantum ensemble learning is proposed to find an approximate solution to a partial differential equation with good precision and favorable scaling in the required number of qubits. The classical component consists in training several regressors (weak-learners), capable of solving a partial differential equation approximately using machine learning. The quantum component consists in adapting the QBoost algorithm to solve regression problems to build an ensemble of classical learners. We have successfully applied our framework to solve the 1D Burgers’ equation with viscosity, showing that the quantum ensemble method really improves the solutions produced by classical weak-learners. We also implemented the algorithm on the D-Wave Systems, confirming the good performance of the quantum solution compared to the simulated annealing and exact solver methods.

Investigating the role of the carbon tax and loss of power supply probability in sizing a hybrid energy system, economically and environmentally

Today, the use of renewable energy-based hybrid energy systems is widely welcomed due to their reduced environmental impact. Therefore, it is vital to optimally design these systems in terms of economy, reliability, and CO2 emissions. This work presents a new method based on Newton's Law of Gravity to achieve the above goals. For this purpose, the role of the carbon tax and the loss of power supply probability (LPSP) in sizing the hybrid energy systems is evaluated. Hence, distinct combinations of wind turbines, photovoltaic panels, and diesel generators are optimized by providing single and multi-purpose functions. Also, to produce cleaner energy, the carbon tax is enacted as a penalty function to control CO2 emissions. Furthermore, in this work, 45 cases are analyzed for achieving an optimal framework and some of the results obtained by the gravitational search algorithm (GSA) method are compared with the simulated annealing (SA) method. Therefore, according to the meteorological data, a PV/Wind/DG system with a total annual cost of 56,175 $/year and CO2 emission of 110,086 kg/year is suggested for the study area. In this case, by claiming a carbon tax of 0.05 $/kg, about 9 % of CO2 emission is prevented, and damage to human health is reduced by 8.9 %. Also, the outcomes display that more than 95 % of carbon dioxide emission is associated with using a diesel generator.

Atomic and electronic structure of grain boundaries in a-Al2O3: A combination of machine learning, first-principles calculation and electron microscopy

To accurately determine the atomic and electronic structures of symmetric tilt grain boundaries (GBs) in α-Al2O3, this work employed an artificial-neural-network (ANN) interatomic potential, density-functional-theory (DFT) calculation and scanning transmission electron microscopy (STEM) observation. An ANN-based simulated annealing method was demonstrated to efficiently screen candidate low-energy structures with reasonably high accuracy. For Σ7 and Σ31GBs with the [0001] tilt axis, which were absent in the training datasets for the ANN potential, their lowest-energy structures predicted from ANN and DFT calculations were in quantitative agreement with STEM images in terms of both Al- and O-column positions. The exact GB structures have enabled us to analyze quantitatively the relationship between their atomic and electronic structure. This work will be an important model case where a combination of machine-learning, theoretical calculation and experiment has successfully solved the problem of determining complicated GB structures and their electronic structures in α-Al2O3.