Simulated Annealing Method Research Articles

Enhancement of CO2 monitoring in the sleipner field (north sea) using seismic inversion based on simulated annealing of time-lapse seismic data

The primary aim of this research is to enhance seismic data interpretation and CO2 monitoring by utilizing seismic inversion techniques based on the simulated annealing method. Simulated annealing is a global optimization technique employed for inverting seismic data and provides better results as compared with local optimization-based inversion. This methodology is implemented in the Utsira Formation, located at a depth of 1000 m within the Sleipner Field, Norway. The study encompasses the analysis of three sets of time-lapse seismic data, first from 1994 (pre-injection), followed by surveys in 1999 and 2001, corresponding to the injection of 2.35 million tonnes and 4.26 million tonnes of CO2, respectively. Firstly, synthetic data is used to check the reliability of the algorithm followed by real data application. This process starts by performing the inversion analysis on the synthetic data which shows a decrease in the impedance values observed at the injection site whereas the seismic amplitude increases. The qualitative as well as quantitative analysis depicts that the algorithm works satisfactorily. The same process is applied to the real data from the Sleipner field. Acoustic impedances are calculated using a simulated annealing-based inversion scheme for the pre-injection case in 1994 and post-injection scenarios in 1999 and 2001. Because of the presence of injected CO2 in the years 1999 and 2001, a low impedance zone that ranged from 2000 m/s*g/cc to 2400 m/s*g/cc appeared at the time interval of 0.85–1.10sec. The interpretation of the inverted impedance section and seismic attribute analysis show no signature of CO2 leakage. The results indicated that the inverted section which is derived from the SA optimization technique shows very clear CO2 information offering a more realistic representation with enhanced resolution of the CO2 plume and its migratory paths.

Optimizing the Location of Supports under a Monolithic Floor Slab

Monolithic reinforced concrete floor slabs are one of the most common types of building structures, and their optimization is an urgent task. The article presents the methodology for finding the optimal position of point supports under a reinforced concrete floor slab of arbitrary configuration at arbitrary load. The slab is considered thin, elastic and isotropic, with constant over-the-area stiffness, that is, the reinforcement is not taken into account or is constant. The solution is performed using the finite element method in combination with the nonlinear optimization methods. Finite element analysis is implemented by authors in MATLAB (R2024a) environment in such a way that the location of the columns may not coincide with the nodes of the finite element mesh of the slab. This allows to significantly increase the efficiency of solving the optimization problem compared to previously used algorithms, including the Monte Carlo method. Boundary conditions are taken into account using the Lagrange multiplier method. As an optimization criterion, the maximum deflection value is used, as well as the value of the potential strain energy. The effectiveness of six nonlinear optimization methods is compared in the example of a square slab under the action of a uniformly distributed load. For solutions obtained using the pattern search, simulated annealing and internal point methods, the maximum deflections are at least 1.2 times higher than for solutions obtained using the particle swarm method and genetic algorithm. An example of real object optimization is also presented. By changing the position of seven columns, it was possible to reduce the maximum deflection of the floor slab by 1.6 times.

A simulated annealing metaheuristic approach to hybrid flow shop scheduling problem

This study investigates a complex hybrid flow shop scheduling problem prevalent in the industrial sector, characterized by dedicated machines, availability dates, and delivery times. The primary objective is to minimize the total completion time (makespan) in a two-stage workshop setting. We conducted a comprehensive literature review, revealing a scarcity of research on this specific configuration, and employed the Simulated Annealing metaheuristic as our main resolution method. Special emphasis was placed on the meticulous parameterization and configuration of this metaheuristic, crucial for navigating the complexity of the problem.Our findings demonstrate the remarkable effectiveness of the Simulated Annealing method, particularly in achieving low deviation from the lower bound in larger problem sizes and specific instance classes. This consistency highlights the method’s robustness and suitability for complex scheduling scenarios. The study also reveals varying degrees of problem solvability across different instance classes, with computation times generally reasonable except in more challenging scenarios.

Automatic generation method for long-focal-length unobscured freeform optical systems with small volume

The existing design methods for long-focal-length unobscured freeform systems rarely consider the imaging quality requirements and volume constraints simultaneously, causing most of the final designs to not fulfill the requirement of light weight. This study proposes a method to automatically design a long-focal-length unobscured reflective system that satisfies volume constraints while maintaining high imaging quality. First, a method to adaptively set the structural parameter range is proposed, and multiple parameters for different systemic specifications can be effectively calculated within it. Subsequently, the systemic volume and area functions are constructed using the ray tracing method, where the tilt angles, distances between mirrors, and radii of curvature of the mirrors are chosen as the optimization parameters. Third, a comprehensive objective function is jointly established combining ray obscuration and convergence as performance evaluation factors. Then, the structural parameters of a long-focal-length unobscured system with small volume are easily obtained via the simulated annealing method. Finally, the improved W-W method is used to further enhance the imaging quality of the system, and an unobscured freeform reflective optical system with three mirrors is automatically generated. Experimental results demonstrate that our method can automatically calculate the parameter ranges to facilitate the search for structural parameters, and effectively design the long-focal-length unobscured freeform systems with small volume and high imaging quality.

Multi-Objective Optimization in 3D Floorplanning

Three-dimensional integrated circuits can significantly mitigate the challenges posed by shrinking feature sizes and enable heterogeneous integration. This paper focuses on the 3D floorplanning problem. We formulate it as a multi-objective optimization issue and employ multi-objective simulated annealing to simultaneously optimize area, wirelength and number of vias. During the optimization process, neighboring solutions are explored in the design space through inter-layer or intra-layer perturbations, and decision criteria for the exploration process are formulated based on the dominance relationship of solutions. Test results on the GSRC benchmark demonstrate that our approach delivers superior performance in optimizing area and wirelength. Compared to 2D floorplanning, our method reduces the area by approximately 49% and the wirelength by 21%. Compared to other similar 3D floorplanning methods, we raise the success rate in satisfying the fixed-outline constraint to 100% and improve the wirelength by 3%. The multi-objective simulated annealing method proposed in this paper can effectively address the 3D floorplanning problem.

An interactive method based on multi-objective optimization for limited-angle CT reconstruction

Objective. Limited-angle x-ray computed tomography (CT) is a typical ill-posed inverse problem, leading to artifacts in the reconstructed image due to the incomplete projection data. Most iteration CT reconstruction methods involve optimization for a single object. This paper explores a multi-objective optimization model and an interactive method based on multi-objective optimization to suppress the artifacts of limited-angle CT. Approach. The model includes two objective functions on the dual domain within the data consistency constraint. In the interactive method, the structural similarity index measure (SSIM) is regarded as the value function of the decision maker (DM) firstly. Secondly, the DM arranges the objective functions of the multi-objective optimization model to be optimized according to their absolute importance. Finally, the SSIM and the simulated annealing (SA) method help the DM choose the desirable reconstruction image by improving the SSIM value during the iteration process. Main results. Simulation and real data experiments demonstrate that the artifacts can be suppressed by the proposed method, and the results were superior to those reconstructed by the other three reconstruction methods in preserving the edge structure of the image. Significance. The proposed interactive method based on multi-objective optimization shows some potential advantages over classical single object optimization methods.

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

Application of CSA to loading pattern optimization problems for reload core in nuclear power plant

Reload loading pattern design is the key content to economy, safety and flexibility of the operation after reloading in nuclear power plant. Loading pattern optimization is a typical combinatorial optimization problem with multi-objective and multi-parameter. Furthermore, the reload loading pattern optimization design is a very short time requirement, which makes it difficult for traditional optimization algorithms. In this study, a combined simulated annealing algorithm (CSA algorithm) are proposed. CSA effectively utilizes the characteristic regularity in the optimization space. Additionally, the combined database created by CSA can significantly increase the diversity of the algorithm, and greatly improve the optimization ability. In comparison to the traditional simulated annealing method, the optimization results of CSA are noticeably better. By contrasting the CSA with the improved genetic algorithm (GA), it can be seen that CSA can exploit the characteristic regularity of the reload loading pattern optimization more effectively, and the optimization efficiency and optimization results of the method are noticeably superior to GA.

Optimal Placement of Superconducting Magnetic Energy Storages in a Distribution Network with Embedded Wind Power Generation

The prevalence of distributed generation in most power grids can negatively affect their performance in terms of power loss, voltage deviation, and voltage stability. Superconducting Magnetic Energy Storages (SMESs) can help in addressing this problem as long as they are optimally placed in the distribution network. This paper presents a hybrid Grasshopper Optimization Algorithm and a Simulated Annealing (GOA-SA) method to determine the optimal placement of SMESs in a distribution network with an embedded wind power generation system. The optimization was formulated as a multi-objective problem to minimize active power losses, reactive power losses, and voltage deviation and maximize the voltage stability index. An IEEE 57-node distribution network was employed and simulations were performed using MATLAB R2020b. Based on simulations using 200 kW SMESs in discharge mode, the active power loss decreased by 82.57%, the reactive power loss decreased by 80.71%, the average voltage deviation index decreased by 66.91%, and the voltage stability index improved by 34.97%. In the charging operation mode, the active power loss increased by 24.86%, the reactive power loss increased by 8.21%, the average voltage deviation increased by 12.86%, and the voltage stability index increased by 12.79%. These results show that SMESs can improve the technical performance of a distribution network.

Efficient heuristic approach for minimization of phonon mean free path in large-area nanostructured thin films

Phonon transport simulations are conducted to unveil the design of nanostructured thin films with the lowest thermal conductivity for enhancing thermoelectric performance. An efficient and effective optimization method that utilizes simulated annealing is realized by tuning and switching a reduction rate of annealing temperature, which is a parameter to facilitate escaping local optima during the process. The superiority of this optimization approach is confirmed by demonstrating it in nanostructured thin films of various sizes. Furthermore, the characteristics of structures with lower thermal conductivities are identified from the optimization results for each size. Based on this, a large-area nanostructured thin film, in which the simulated annealing method is computationally costly, is designed and validated by comparing it with typical nanostructured thin films as a reference.

Hybrid genetic algorithm-simulated annealing based electric vehicle charging station placement for optimizing distribution network resilience

Rapid placement of electric vehicle charging stations (EVCSs) is essential for the transportation industry in response to the growing electric vehicle (EV) fleet. The widespread usage of EVs is an essential strategy for reducing greenhouse gas emissions from traditional vehicles. The focus of this study is the challenge of smoothly integrating Plug-in EV Charging Stations (PEVCS) into distribution networks, especially when distributed photovoltaic (PV) systems are involved. A hybrid Genetic Algorithm and Simulated Annealing method (GA-SAA) are used in the research to strategically find the optimal locations for PEVCS in order to overcome this integration difficulty. This paper investigates PV system situations, presenting the problem as a multicriteria task with two primary objectives: reducing power losses and maintaining acceptable voltage levels. By optimizing the placement of EVCS and balancing their integration with distributed generation, this approach enhances the sustainability and reliability of distribution networks.

A two-magnet energy harvesting device with buoy base for a marine terminal

It is crucial to explore new green energy sources to alleviate the earth’s energy depletion and the impact of air pollution and greenhouse effects. Although wafer manufacturing is increasing worldwide, the implementation of carbon footprint policies necessitates a shift toward green electricity. To expand the scale of wafer manufacturing, it is essential to develop terrestrial green electricity, which requires additional terrestrial green energy sources. This paper presents a green energy harvesting method that generates wave hydraulic energy via a vibration-based electromagnetic generator through a buoy. An energy harvester composed of two magnets in series driven by a buoy via a connected pole is introduced. The sensitivity of induced electricity to the energy harvester’s design parameters, including the magnets’ geometric dimensions (diameter and height), the coil’s turns, and layers, is analyzed. Additionally, the relationship between electricity and wave information (wave amplitude and wavelength) is explored. To evaluate the electrical power efficiency between a pair of single-magnet energy harvesters and the two-magnet harvester, a pair of parallel energy harvesters consisting of one magnet and paralleled in line is assessed. The impact of electricity on the horizontal span between the parallel energy harvesters is also investigated. To achieve maximal electricity, the two-magnet energy harvester is numerically optimized using a simulated annealing method. The numerical result shows that the two-magnet (in series) energy harvester can produce 0.472 W when the wavelength is 0.03, the amplitude is 0.09 m, and the wave speed is 3 m/s.

Incompatibility stress at inclined grain boundaries for cubic crystals under hydrostatic stress and uniaxial stress

In a material under stress, grain boundaries may give rise to stress discontinuities. The stress state at grain boundaries strongly affects microscopic processes, such as diffusion and segregation, as well as failure initiation, such as fatigue, creep, and corrosion. Here the general condition of incompatibility stress at grain boundaries is studied with a bicrystal model for linear elastic materials. In materials with cubic crystal structures, it is proven that hydrostatic stress does not lead to a stress discontinuity at grain boundaries. For bicrystals with inclined grain boundaries under uniaxial stress, the extreme values of the incompatibility stress as a function of the inclination angle are obtained by a simulated annealing method. A simple criterion is proposed to classify cubic materials into three groups. For cubic crystals with at most moderate anisotropy, the highest incompatibility stress occurs when the grain boundary plane is perpendicular to the uniaxial stress. For highly anisotropic materials, such as alkali metals and polymorphic high-temperature phases, the highest incompatibility stress occurs on grain boundaries with an inclination of about 47o.

Matheuristics vs. metaheuristics for joint lot-sizing and dynamic pricing problem with nonlinear demands

This paper considers a joint dynamic pricing and production planning decisions problem for a profit-maximizing firm that produces and sells multiple products. The objective is to develop a coordinated decision approach for multi-product pricing and lot sizing decisions for a manufacturer considering a limited production capacity. The demand for each product is assumed to be iso-elastic and integrates the complementarity and the substitution effects between the products. First, the problem is formulated as a non-convex mixed integer nonlinear programming model (MINLP) incorporating capacity constraints, setup costs, and nonlinear demand functions. Then, since the model is nonlinear and non-convex, a set of approximate approaches based on the Genetic algorithm, Late Acceptance Hill Climbing and Simulated Annealing methods are designed to solve this problem. Based on this study, the performances of two variants of approximate methods: matheuristics and metaheuristics are discussed and analyzed. The extensive experimental study, performed on real-world inspired instances, shows that matheuristic methods with setup-variables encoding scheme outperform the rest of the methods. The research outcomes show that coordinating a decision-making process by optimizing both prices and production plans simultaneously can result in significant profit for a company. However, one must consider the joint effect of the parameters of the demand function as well as the impact of the production capacity. This comprehensive understanding enables the company to avoid excessive investments in less lucrative products with lower sales potential, thereby ensuring resource allocation aligns with profitability and market demand.

Optical properties of InSb derived from reflection electron energy loss spectroscopy spectrum

The energy loss function (ELF) of the narrow bandgap semiconductor, InSb, was derived from the reflection electron energy loss spectroscopy (REELS) spectrum measured at 4 keV incident electron energy in an energy loss range of 1.6–200 eV using a high energy resolution electron spectrometer. The experimental spectrum was analyzed with the reverse Monte Carlo (RMC) method, which integrates the classical trajectory Monte Carlo simulation with the simulated annealing method for optimizing the trial ELF. Subsequently, the complex dielectric function ε(ω), refractive index and extinction coefficient were determined from the obtained ELF in the energy loss (i.e. photon energy) range of 1.6–200 eV. The validity of the obtained data was verified by the f-sum rule, ps-sum rule, inertial sum rule and dc-conductivity sum rule. We found that our calculated data of InSb fulfill the sum rules with very high accuracy; therefore, the use of these calculated optical data in material science and surface analysis is highly recommended for further applications.

Accurate solution of the Index Tracking problem with a hybrid simulated annealing algorithm

An actively managed portfolio almost never beats the market in the long term. Thus, many investors often resort to passively managed portfolios whose aim is to follow a certain financial index. The task of building such passive portfolios aiming also to minimize the transaction costs is called Index Tracking (IT), where the goal is to track the index by holding only a small subset of assets in the index. As such, it is an NP-hard problem and becomes unfeasible to solve exactly for indices with more than 100 assets. In this work, we present a novel hybrid simulated annealing method that can efficiently solve the IT problem for large indices and is flexible enough to adapt to financially relevant constraints. By tracking the S&P-500 index between the years 2011 and 2018 we show that our algorithm is capable of finding optimal solutions in the in-sample period of past returns and can be tuned to provide optimal returns in the out-of-sample period of future returns. Finally, we focus on the task of holding an IT portfolio during one year and rebalancing the portfolio every month. Here, our hybrid simulated annealing algorithm is capable of producing financially optimal portfolios already for small subsets of assets and using reasonable computational resources, making it an appropriate tool for financial managers.

Prediction of CBR by Deep Artificial Neural Networks with Hyperparameter Optimization by Simulated Annealing

AbstractThe construction of pavements requires the complete identification of the soils in place and of the added materials. This identification consists in determining the class of the soils and in evaluating their bearing capacity through the California bearing ratio (CBR) index. Obtaining the CBR index is very costly in terms of time and financial resources, especially when it is a large-scale project. It thus leaves prospects of obtaining it by simpler processes; hence, it arises the need to find simpler processes compared to classical processes. This study develops models for predicting the CBR index from physical properties that are less complex to obtain, based on deep neural networks. To achieve this, three databases were used. A first database consists of the proportion of fines, the Atterberg limits and the Proctor references of the soils. A second database uses the methylene blue value instead of the Atterberg limits, and a third database uses only the proportion of fines and the Proctor soil reference. On each of the databases, a deep neural network model was developed using dense layers, regularization layers, residual blocks and parallelization in TensorFlow to predict the CBR value. Each model was formed by combining several deep neural networks developed according to specific architectures. To expedite training, the simulated annealing method was employed to optimize hyperparameters and define the optimal configuration for each network. The predictions obtained are correlated with the true values from 83.6 to 96.5%. In terms of performance, the models have a mean deviation ranging from 3.74 to 5.96%, a maximum deviation ranging from 12.43 to 16.2% and a squared deviation ranging from 0.781 to 2.189. The results suggest that the variable VBS has a negative impact on the accuracy of the networks in predicting the CBR index. The developed models respect the confidence threshold (± 10%) and can be used to set up a local or regional geotechnical platform.

Optimal arrangement of heliostat field based on simulated annealing algorithm

In this study, a simulated annealing algorithm is proposed as an optimisation method for the mirror arrangement of heliostat mirrors in a tower solar thermal power plant. Firstly, the optimal number and position of heliostats in the whole mirror field in radial staggered layout (EB unobstructed layout) were solved using simulated annealing method. Then further optimisation, i.e. changing the mirror width, mirror height and mounting height of the heliostats and solving for the optimal number and position of heliostats using simulated annealing again, resulted in an increase in the number of heliostats in the optimised mirror field by 77.92% but a decrease in the total area by 18.77%, and an increase in the average annual thermal power output per unit area of the mirrors by 4.76%. Therefore, this result shows that the heliostat field optimised using the simulated annealing algorithm has a higher efficiency and a smaller footprint.

Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity.

Stable and efficient seismic impedance inversion using quantum annealing with L1 norm regularization

Abstract Seismic impedance inversion makes a significant contribution to locating hydrocarbons and interpreting seismic data. However, it suffers from non-unique solutions, and a direct linear inversion produces large errors. Global optimization methods, such as simulated annealing, have been applied in seismic impedance inversion and achieved promising inversion results. Over the last decades, there has been an increasing interest in quantum computing. Owing to its natural parallelism, quantum computing has a powerful computational capability and certain advantages in solving complex inverse problems. In this article, we present a stable and efficient impedance inversion using quantum annealing with L1 norm regularization, which significantly improves the inversion accuracy compared to the traditional simulated annealing method. Tests on a one-dimensional 10-layer model with noisy data demonstrate that the new method exhibits significantly improved accuracy and stability. Additionally, we perform seismic impedance inversion for synthetic data from the overthrust model and field data using two methods. These results demonstrate that the quantum annealing impedance inversion with L1 norm regularization dramatically enhances the accuracy and anti-noise ability.