Simulated Annealing Algorithm Research Articles

An effective hybrid meta-heuristic method for the simultaneous batch production and transportation problem in additive manufacturing

One notable advancement in additive manufacturing (AM) is the mobile mini-factory, which uses a truck equipped with an AM machine to produce orders while en-route to customers' locations. This offers potential benefits such as reduced delivery times and storage expenses for companies. This study investigates a simultaneous batch production and transportation problem (denoted by SBPTP) in additive manufacturing. To solve this problem, a mixed integer linear programming (MILP) model is first formulated. Then, to solve large-scale problems, a meta-heuristic method (denoted by SA-CP) combining a simulated annealing (SA) algorithm, an ant colony optimisation algorithm (ACO) and a cutting-plane algorithm is developed, in which the assignment subproblem is dealt with the SA, the simultaneous production and transportation subproblem is dealt with the ACO, and finally the current solution is further improved by the cutting-plane algorithm. Computational experiments are conducted on both randomly generated instances and modified benchmark instances. The results demonstrate that the SA-CP is very effective since it can obtain the solutions with an average relative percentage gap 0.16% on randomly generated instances and −0.09% on modified benchmark instances within less than 180 CPU seconds, compared to those obtained by solving the MILP model directly with CPLEX within 1 h.

Medical image segmentation based on simulated annealing and opposition-based learning island algorithm.

With the development of society and changes in the human living environment, people are increasingly attaching importance to their own health. Regarding medical imaging examinations of certain parts of the body, the process of medical image segmentation has become extremely important. This paper presents a novel hybrid algorithm: SAOBL-IA, a fusion of the Simulated Annealing(SA), Opposition-based Learning(OBL)and Island Algorithm(IA). The Island Algorithm itself suffers from slow convergence speed and the tendency to get stuck in local optimum. To address these limitations, we introduce opposition-based learning to enhance the search range and avoid local optimum. Furthermore, we leverage the simulated annealing approach to accelerate the convergence of SAOBL-IA. Comparing the experimental results, it can be seen that SAOBL-IA has better comprehensive performance. Subsequently, the SAOBL-IA algorithm is utilized in conjunction with an optimized two-dimensional OTSU fusion segmentation technique for the purpose of medical image processing. This study proposes an application of image segmentation based on the SAOBL-IA. The segmentation of pixels around the background and target regions using the two-dimensional OTSU method faces challenges in terms of accuracy. To address this issue, an adaptive thresholding technique known as Adaptive Forking is employed for optimization. By determining the slope of the fork based on the misclassified pixel ratio, enhanced segmentation accuracy can be achieved. This improved approach is then integrated with the SAOBL-IA algorithm and applied to the segmentation of lung medical images. The experimental findings show that the amalgamation of SAOBL-IA with the adaptive two-dimensional OTSU segmentation approach, as proposed in this study, manifests superior segmentation speed and enhanced precision in the context of medical image segmentation.

Opportunity Analysis of Phosphorus Recovery from Municipal Wastewater for Cropland Based on the Simulated Vehicle Transport Distance in the Yangtze River Delta, China.

With the rapid depletion of phosphate rocks and increasing agricultural demand, establishing a phosphorus (P) flow "loop" rather than a one-way trajectory between cropland and urban areas was imperative. Recovering P from municipal wastewater stood as a viable strategy to mitigate reliance on traditional P-containing chemical fertilizer. This study analyzed the intricate relationships between the potentials of P recovery from municipal wastewater and the P demand of croplands in the populated Yangtze River Delta (YRD), China. An indicator of the P vehicle transport distance was constructed and calculated to estimate the potential to recover and reuse P in agriculture, applying the simulated annealing (SA) algorithm and road networks obtained from OpenStreetMap (OSM). The results indicated that, on a regional scale, recovered P from municipal wastewater could fulfill 14.0% of the cropland P demands in the YRD, with a median P vehicle transport distance of 3.1 km/Mg of P. Notably, the P vehicle transport distance varied largely depending upon the cropland distributions, road density, and P recovery potential from municipal wastewater. The novel methodology developed here determined the optimal transportation routes for P recovery from wastewater treatment plants (WWTPs) to cropland, which played a crucial role in refining the wastewater management strategies aligned with the United Nations Sustainable Development Goals.

Multi-objective optimization design of NPR protection shell for hydrogen storage tank

In order to improve the safety of on-board hydrogen storage tanks during collision, a protective shell based on a negative Poisson’s ratio (NPR) core is designed in this paper. After analyzing and comparing the crashworthiness of three typical honeycomb structures, the concave hexagonal negative Poisson’s ratio honeycomb is selected as the energy-absorbing inner core of the hydrogen storage tank protection structure. The sensitivity analysis of the structural parameters of the NPR shell is conducted through orthogonal tests to identify parameters with a significant impact on crash performance. These parameters are then used as experimental variables for subsequent optimization design. Subsequently, a response surface approximation model between the optimization objective and the structural parameters is established based on the response surface method. Finally, the adaptive simulated annealing algorithm (ASA), neighborhood cultivation genetic algorithm (NCGA), and non-dominated sorting genetic algorithm-II (NSGA-II) are used to optimize the structural parameters, and the optimization results are verified by the whole-vehicle crash simulation. The simulation results demonstrate that all three algorithms achieve better optimization results, among which NCGA is more advantageous in improving the overall performance of the protective shell. After NCGA optimization, the specific energy absorption of the protective shell increases by 19.75%, the maximum collision force of the rigid wall decreases by 23.63%, and the maximum stress of the hydrogen storage tank body decreases by 22.77%. These findings indicate that the designed protection shell effectively improves the crashworthiness of the hydrogen storage tank during collisions.

PVDF/Al2O3/Gd2O3 layers splitter with first-order suppression functionality

In the realm of optical component design, the proposed multilayer grating splitter represents a significant breakthrough. For the triple-layered grating in this paper, it combines the outstanding optical properties of polyvinylidene fluoride (PVDF), aluminum oxide (Al2O3), and gadolinium oxide (Gd2O3) to achieve high-efficiency outputs for both the 0th and −2nd orders under secondary Bragg angle incidence conditions. The diffraction efficiencies for both TE and TM polarizations exceed 97%, with energy uniformity surpassing 99%. These results highlight the potential of this design in precision beam control applications. The splitter's performance benefits from the intrinsic material properties and optimally designed structural dimensions, which enhance transparency and thermal stability under operational conditions. Through rigorous design and optimization, including modal methods and simulated annealing algorithms, the device achieves exceptional uniformity in diffraction efficiency, which is crucial for high-resolution spectroscopy and precision optical measurement applications.

Optimization Design of Laser Arrays Based on Absorption Spectroscopy Imaging for Detecting Temperature and Concentration Fields.

Detecting temperature and concentration fields within engine combustors holds paramount significance in enhancing combustion efficiency and ensuring operational safety. Within the realm of engine combustors, the laminar absorption spectroscopy technique has garnered considerable attention. Particularly crucial is the optimization of the optical path configuration to enhance the efficacy of reconstruction. This study presents a flame parameter field reconstruction model founded on laminar absorption spectroscopy. Furthermore, an optimization approach for refining the optical path configuration is delineated. In addressing non-axisymmetric flames, the simulated annealing algorithm (SA) and Harris's Hawk algorithm (HHO) are employed to optimize the optical path layout across varying beam quantities. The findings underscore a marked reduction in imaging errors with the optimized optical path configuration compared to conventional setups, thereby elevating detection precision. Notably, the HHO algorithm demonstrates superior performance over the SA algorithm in terms of optimization outcomes and computational efficiency. Compared with the parallel optical path, the optimized optical path of the HHO algorithm reduces the temperature field error by 25.5% and the concentration field error by 26.5%.

Recognition and Prediction of Multi-Level Handling Complexity at Automated Terminals Based on ARIMA

Accurate recognition and prediction of the multi-level handling complexity in automated container terminals (referred to as “automated terminals”) is a prerequisite for improving the effectiveness of scheduling and realizing intelligent operation and maintenance. According to the operating characteristics of the automated terminal equipment, the operating network is constructed of automated terminals that describe the characteristics of operating complexity. We use K-medoids and a light gradient boosting machine (LightGBM) to construct a K-LightGBM model that recognizes multi-level handling complexity. The key lies in the reasonable construction of prediction models. A hyper-heuristic autoregressive integrated moving average (ARIMA) model is proposed to address the problem that the ARIMA is ineffective in predicting nonlinear data. We combine ARIMA and the LightGBM model to establish an ARIMA-LightGBM model to predict multi-level handling and residuals. To improve accuracy, we propose the two residual prediction strategies of direct prediction and limited residual boundary prediction based on the residuals generated by ARIMA. We propose a hyper-heuristic algorithm based on a gradient descent-trust region (GD-TR) to compute the weights of predicted values under the two strategies, which improves the global search capability by GD and TR. The particle swarm optimization algorithm, simulated annealing algorithm, and ant colony optimization algorithm are low-level heuristics. Simulation results show that the proposed model possesses the lowest root mean square error on all characteristics compared to ARIMA, E-ARIMA, and ARIMA-LSTM. Therefore, the proposed model is very effective in improving the accuracy of predicting the multi-level handling complexity at automated terminals.

Level of thermal maturity estimation in unconventional reservoirs using interval inversion and simulating annealing method

AbstractThis study presents a novel geophysical approach for estimating the level of thermal maturity (LOM) in unconventional hydrocarbon reservoirs using well log data. LOM is a crucial parameter for assessing the hydrocarbon generation potential of source rocks, but it traditionally relies on laboratory measurements of core samples, which can be time-consuming and costly. The proposed method combines two techniques: interval inversion for estimating total organic carbon (TOC) content from well logs and simulated annealing (SA) optimization for deriving LOM from the estimated TOC. The interval inversion method enables accurate TOC estimation by jointly interpreting multiple well logs over depth intervals, overcoming limitations of conventional point-by-point inversion. Using the estimated TOC, the SA algorithm optimizes an energy function related to Passey's empirical TOC-LOM relationship, iteratively finding the optimal LOM value that best fits the well log data. This approach provides a continuous in situ LOM profile along the borehole without requiring core measurements. The effectiveness of the method is demonstrated through case studies on datasets from the North Sea (Norway), the Pannonian Basin (Hungary), and the Kingak Formation (Alaska). The LOM estimates show good agreement with reported maturity levels and allow reliable reservoir characterization. Statistical analysis confirms the robustness and accuracy of the results. By reducing dependence on core data, this integrated inversion-optimization workflow streamlines the reservoir prospecting phase, enhancing operational efficiency. The method holds promising applications across diverse geological settings for cost-effective evaluation of unconventional hydrocarbon plays.

Noninvasive reconstruction of internal heat source in biological tissue using adaptive simulated annealing algorithm

The heat distribution information of human lesions is of great value for disease analysis, diagnosis, and treatment. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. This paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the adaptive simulated annealing (ASA) algorithm is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the sum of absolute values of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the simulated position and temperature of the heat source are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

Furniture design based on image color extraction algorithm

With the increasing demand for personalized and customized home products, how to realize the innovative design of furniture and improve the design efficiency has become a research hotspot for related professionals. Aiming at these problems, the study extracts the main color of furniture images by optimizing the K-mean clustering algorithm, uses the simulated annealing algorithm to color-match the furniture, and reconstructs the image by edge detection to design a furniture design method based on image color extraction. The results revealed that in the foreground part, the correct rate of color match based on the design method was 95.7%, and in the background part, the correct rate of color match based on the design method was 94.81 %, which proved its effectiveness. The average feature point extraction time and the average feature point matching time of the design-based algorithm were 5.45 ms and 9.83 ms, respectively, which proved its high computational efficiency. In furniture color edge detection and overall color match, the image obtained based on the design method was significantly clearer, and the overall coherence, saturation and brightness were closer to the input image. In addition to raising the standard of furniture design, the study's design methodology increases design efficiency and offers solid technical support for the area.

Study on updating finite element model of steel truss structure based on knowledge-enhanced deep reinforcement learning

Accurate assessment of the damage to a structure is the key to structural health monitoring. Among them, one effective means to assess the real-time status of the structure is by establishing a finite element model (FEM) of the structure and updating it synchronously. Previous research mainly updates the FEM of the structure with a certain moment in time without considering the continuous changes of structural parameters over a longer time. In this paper, we propose a FEM updating method for steel truss structure based on knowledge-enhanced deep reinforcement learning, which introduces long short-term memory (LSTM) network based on a deep deterministic policy gradient (DDPG) algorithm and constructs a knowledge-enhanced noise function (NF) module based on parametric analysis. The NF-LSTM-DDPG algorithm can fit a continuous variation of structural parameters. The ablation experiments revealed that the LSTM network and the NF module facilitate the exploration and learning ability of the DDPG algorithm. The NF-LSTM-DDPG algorithm is used in an actual steel truss structure case, and the variation rules of the elastic modulus and support deviations obtained from the solution are consistent with the actual situation. The average relative error of the 34 strain measurement points after updating is 14.147 %, and the relative error is higher than 20 % in only 13 days out of 187 days of continuous monitoring. Five days were selected for the comparative study, and the experimental results show that the relative error of the NF-LSTM-DDPG algorithm is 13.206 %, which is higher than that of the particle swarm optimization algorithm and the simulated annealing algorithm. The change of elastic modulus updated based on the NF-LSTM-DDPG algorithm is more reasonable than other algorithms.

Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes

This paper presents a unique approach to generate a number of cutting knowledge blocks for the surface roughness analysis of the drilling process for carbon-fiber-reinforced polymer composite (CFRP) materials. The influence of drilling on the surface quality of woven CFRP materials was investigated experimentally. The CFRP material (0/90° fiber orientation) was drilled at different cutting parameters and the surface roughness of the hole was measured. A set of tests was carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds, 2, 3, and 4 flute numbers, and 0.2, 0.3, and 0.4 mm/rev feed rates. The Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. Based on the experimental findings and optimization techniques applied, optimal cutting parameters were derived, which were subsequently adjusted to enhance surface quality. Overall, the cutting parameters are carefully optimized to achieve good surface roughness quality in the drilling of CFRP.

Metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model’s input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire’s perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario.

MIMO optical communication systems based on broadband transmitters and receivers

A multiple-input multiple-output (MIMO) optical communication system is proposed. In the MIMO optical communication system, a series of broadband sources with different spectra act as the transmitters, and a compact imaging chip sensor accompanied by a disorder dispersion component and a calculating component serve as the receivers. Because the spectral ranges of the sources are overlapped, the number of the signal channels is not restricted by the bandwidth of the communication band and the characteristics of the sources. L1-norm regularization, L2-norm regularization, and the simulated annealing algorithms are used to solve matrix equations for decoding the signals at the receivers. A proof-of-concept experiment using on-off keying modulation has been done to prove the feasibility of the design. The experimental results demonstrate that the signals decoded by the receivers fit well with those generated from the transmitters. Besides the function of communications, the broadband sources are suitable to act as illumination sources because the white color lights in the visible band are more comfortable for human eyes compared with monochromatic lights used in traditional visible light communication (VLC) systems. We also prove that the MIMO optical communication system can operate in the infrared band. The infrared communication (IC) system has potential applications in situations requiring communication in a dark environment that cannot be realized by VLC systems.

Recovering induced polarization effects from 1-D coupled inversion of transient electromagnetic data

SUMMARY Induced polarization (IP) effects can significantly affect and superimpose the inductive earth response, leading to heavily distorted data and, if overlooked, false geological interpretation. In this paper, we implemented the Levenberg–Marquardt (LM) and very fast simulated annealing (VFSA) algorithms to recover IP effects from central loop transient electromagnetic (TEM) data. To incorporate the IP effect in the TEM response, we used the Cole–Cole parametrization, maximum phase angle (MPA), maximum imaginary conductivity (MIC) and Jeffrey transform of Cole–Cole parameters. The result of 1-D forward calculation and inversion of synthetic TEM data revealed that the Cole–Cole parametrization is more robust and reliable than MPA, MIC and Jeffrey transform, and that the synthetic data were well fitted and IP parameters well recovered using this model. However, the incorporation of the IP effect leads to a highly nonlinear and non-unique inverse problem which requires an accurate starting model, especially for LM inversion. To evaluate the performance of our algorithm using field data, we carried out a 1-D inversion of TEM data acquired along a profile that traverses a waste site located near Cologne, Germany. Furthermore, to obtain a priori information and validate the result of TEM data modelling, we conducted an electrical resistivity tomography (ERT) and time-domain IP (TDIP) survey along the TEM profile. A 2-D inversion was used to retrieve the Cole–Cole parameters as input for TEM interpretation. By including the IP information, the TEM field data can be explained quantitively, and a consistent and improved interpretation of the waste body is achieved.

Multiple unmanned ship coverage and exploration in complex sea areas

This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

Optimal control of a quantum sensor: A fast algorithm based on an analytic solution

Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the pulsed control field that optimizes the sensitivity (i.e., the smallest detectable signal) can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a lower bound for the sensitivity and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.

Combining simulated annealing and RBF networks for accurate volumetric fraction determination of two-phase flows

The importance of flow monitoring in the oil industry has expanded due to the global need for fossil fuels. This has led to the emergence of a new subset of the flowmeter market. The goal of this study is to use a Radial Basis Function (RBF) neural network developed through Simulated Annealing (SA) to pick features of the signals generated by gamma-based flowmeters in order to determined volumetric fractions. The volumetric detection system presented in this article consists of a137Cs isotope as gamma emitter, two NaI detectors for collecting the photons, and a glass pipe in between them. Monte Carlo N-Particle (MCNP) was used to model the above-mentioned geometry. Fifteen wavelet, frequency, and time characteristics were extracted from the raw data captured by both detectors. First, the SA optimization algorithm was used to identify the suitable attributes. Five useful features were presented as a consequence of this procedure, and they were fed into the RBF network in order to estimate volumetric percentages. This study is innovative in that it combines the RBF neural network with the SA algorithm to pick effective features. The outcome is as follows: (1) Outlining five appropriate characteristics for use in determining percentages of volume. (2) Predicting the volume fraction of materials in two-phase flow with a root mean square error of less than 0.22. (3) By recognizing suitable inputs using the method based on the SA algorithm, the artificial neural network can determine the target output with less computational load.

Modeling and preparation of additive-manufactured laser ceramics

ABSTRACT Additive manufacturing (AM) presents promising prospects for the production of laser ceramics, particularly those with composite structures. However, achieving uniform density within these ceramics is a formidable challenge, primarily due to the intrinsic limitations of uneven forces within the mold. This paper introduces the Density Spatiotemporal Evolution (DSE) model, a novel predictive tool designed to map the density distribution in laser ceramics throughout the AM process. The DSE model elucidates that ceramic density increases in proximity to the indenter and mold wall and reveals an oscillatory pattern in particle density during pressing. These insights have been corroborated through experimental measurements and simulation analyses. Leveraging the DSE model and a Simulated Annealing (SA) algorithm, an innovative AM process is proposed. The pressure exerted to each layer of ceramics is gradient distributed, i.e. 18, 18.36, 19.31, 22.08, 33.02 MPa. Preliminary experimental results indicate a significant enhancement in density uniformity, with an improvement of up to 45% in the optimized ceramics. The advancement promises to yield more homogeneous ceramic materials, potentially accelerating the progress of high-energy laser technology.

Multi-objective optimization of vehicle body B-pillar lower joints based on crashworthiness analysis

The joints in an automobile’s body structure are crucial in bearing loads and transmitting stresses, thereby significantly affecting the body’s rigidity. To effectively improve body rigidity and crashworthiness, this study employed a sensitivity analysis to identify the critical joints among the nine joints of a specific sport utility vehicle (SUV) body. Following regulatory requirements, collision simulations were performed, revealing that the joint below the B-pillar exhibited the most significant deformation. Thus, using the material and thickness of the B-pillar’s lower joint as design variables, experimental samples were generated by the design of experiment (DOE). A multi-objective optimization for the B-pillar’s lower joint model was conducted using the response surface method and the simulated annealing algorithm to determine the final optimized solution. The optimization results showed a 9.31% increase in body bending stiffness, an 11.37% increase in torsional stiffness, and reduced intrusion at various points on the B-pillar, effectively enhancing the body’s rigidity and crashworthiness.