Simulated Annealing Algorithm Research Articles

Optimal selection of manufacturing enterprise financialization based on simulated annealing investment portfolio theory

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.

Deep learning-based inverse design of multi-functional metasurface absorbers

A novel approach—integrating a simulated annealing (SA) algorithm with deep learning (DL) acceleration—is presented for the rapid and accurate development of terahertz perfect absorbers through forward prediction and backward design. The forward neural network (FNN) effectively deduces the absorption spectrum based on metasurface geometry, resulting in an 80,000-fold increase in computational speed compared to a full-wave solver. Furthermore, the absorber’s structure can be precisely and promptly derived from the desired response. The incorporation of the SA algorithm significantly enhances design efficiency. We successfully designed low-frequency, high-frequency, and broadband absorbers spanning the 4 to 16 THz range with an error margin below 0.02 and a remarkably short design time of only 10 min. Additionally, the proposed model in this Letter introduces a novel, to our knowledge, method for metasurface design at terahertz frequencies such as the design of metamaterials across optical, thermal, and mechanical domains.

PSA: an effective method for predicting horizontal gene transfers through parsimonious phylogenetic networks.

Horizontal gene transfer (HGT) from one organism to another, according to some researchers, can be abundant in the evolution of species. A phylogenetic network is a network structure that describes the HGTs among species. Several studies have proposed methods to construct phylogenetic networks to predict HGTs based on parsimony values. Existing definitions of parsimony values for a phylogenetic network are based on the assumption that each gene site or segment evolves independently along different trees in the network. However, in the current study, we define a novel parsimony value, denoted the p definition, for phylogenetic networks, considering that a gene as a whole typically evolves along a tree. Using Simulated Annealing, a new method called the Phylogeny with Simulated Annealing (PSA) algorithm is proposed to search for an optimal network based on the p definition. The PSA method is tested on the simulated data. The results reveal that the parsimonious networks constructed using PSA can better represent the evolutionary relationships of species involving HGTs. Additionally, the HGTs predicted using PSA are more accurate than those predicted using other methods. The PSA algorithm is publicly accessible at http://github.com/imustu/sap.

High-order residual moveout correction with global optimization in local time windows

Residual moveout (RMO) correction is significant for optimizing the common image gathers (CIGs) and migration stacked sections, which is a vital step in seismic data processing. The conventional RMO correction methods flatten the migrated gathers by inverse normal moveout and then pick up the velocity parameters, which require high labor of manual picking with low efficiency. The existing high-order RMO correction methods by scanning technology need a bispectral picking process. To address these issues, we propose a novel high-order RMO correction method based on a crosscorrelation-based objective function with global optimization in local time windows. The selected time windows covering the reflected events in the stacked section are used for parameter estimation. Then the very fast simulated annealing (VFSA) algorithm is used to simultaneously invert the two effective parameters (migration velocity and anellipticity anisotropic parameter) with the local CIGs in the time window, which has relatively high computational efficiency. The Hess model example test results show that the proposed method can effectively deal with the RMO problem in the case of anisotropic media, and the developed high-order RMO correction based on anisotropic assumption significantly improved the imaging performance compared to the conventional isotropic RMO correction methods, especially for steep structures. The application to a real 3D land data set further confirms the effectiveness and practicability of the proposed method.

The Optimization of LSTM Model by Wavelet Transform and Simulated Annealing Algorithm

The main objective of this paper is to study the integration of data processing methods and intelligent algorithms to optimize the results of LSTM prediction models. In this paper, continuous wavelet transform is used to clean and preprocess time series data and improve data quality. Wavelet reconstruction is used to restore the results. At the same time, the simulated annealing algorithm is introduced as an intelligent algorithm to search globally for the best solution to achieve the optimal prediction result. The application of this comprehensive approach can also improve the quality and precision of data analysis in various fields, such as the parameter estimation of pulse signals in physics. The core challenge of the research is to optimize the data prediction results, and for this purpose, a multi-level method of continuous wavelet transform, deep learning model (LSTM) and simulated annealing algorithm is adopted.

Emotional agents enabled bilateral negotiation: Persuasion strategies generated by agents' affect infusion and preference

Agents with emotional intelligence can enhance negotiation outcomes by improving interaction and better understanding of human opponents in human–machine negotiation. This paper proposes a novel method to improve agents' emotional intelligence level by modeling emotion and preferences in negotiation. Emotion is modeled to reflect an agent’s enduring emotional state and its dynamically instantaneous response to the opponent’s proposals. Emotion-driven rules are subsequently constructed to generate persuasion strategies. Then, a two-step issue updating model which integrates persuasion strategies, attribute preference, and altruistic preference is established to generate optimal issue values using the simulated annealing algorithm. After that, the proposed model is validated by agent-agent experiments and human-agent experiments. The experimental results show that (1) the introduction of emotion and persuasion strategies enhances negotiation success, with joint utility rising by 11%, utility difference decreasing by 28%, and negotiation speed rising by 24%.; (2) the inclusion of preference reduce the utility difference and improve the negotiation fairness, with attribute preference and altruistic preference reducing utility difference by 12% and 18%, respectively; (3) the proposed model outperforms competing models in negotiation speed and joint utility, and ensures larger joint utility and minimizing differences even when opponent's preferences are unknown. The results imply that the proposed model is conducive to achieving a win–win human-agent negotiation outcome.

Optimization of the on-power refueling strategy for the slightly enriched uranium core of Atucha-II NPP

The development of an on-power refueling strategy requires meticulous consideration due to its impact on relevant reactor parameters during operation. The rapid increase in computational power promotes the use of heuristic (or metaheuristic) methods for optimizing systems with numerous core calculations, such as designing a refueling strategy. Building on the previous success of optimizing the on-power refueling strategy for the Atucha-I core using the Simulated Annealing algorithm, the approach was extended to design an enhanced refueling strategy for the future use of slightly enriched uranium fuel in Atucha-II. Previous efforts manually achieved an acceptable refueling strategy, consistent with 3 irradiation paths, each having 3 burnup zones. The primary goal of the optimization was to reduce the usage of the refueling machine, a critical component for plant availability and performance. To achieve this, the number of burnup zones for the optimization was set at 2 per irradiation path. An acceptable optimized refueling strategy was obtained and, subsequently, manual refinements were introduced to improve the time-average channel power distribution. To assess the feasibility of the obtained 2-burnup-zones per path refueling strategy, a simulation of the reactor operation including on-power refueling consistent with this refueling strategy was conducted for 4 years at full power. The simulation predicts a 25.6 % reduction in the refueling machine’s use at full power and savings of 2.9 fuel assemblies per year compared to operation with the 3-burnup-zones per path refueling strategy. The compelling advantages in the performance of the 2-burnup-zones per path refueling strategy have led to its formal adoption as the new official refueling strategy for the design phase of the use of slightly enriched uranium fuel in the Atucha-II core.

A multi-objective optimized OLSR routing protocol.

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.

Automated pricing and replenishment strategies for vegetable products based on SVR and simulated annealing

In fresh supermarkets, the shelf life of general vegetable products is relatively short, and the quality deteriorates with the increase of sales time, and most varieties cannot be sold the next day if they are not sold on the same day. However, due to the fact that the vegetables sold by fresh supermarkets often have a wide variety and different origins, merchants need to make replenishment decisions for each vegetable category on the same day without accurately judging the specific single product and purchase price, and the reliability of market demand analysis is very important for supermarkets to make correct replenishment and pricing decisions. Based on the relevant data of sales details and wholesale prices of six vegetable commodities operated by a supermarket from 2020 to 2023, this paper uses Fourier series and Gaussian function to predict the sales volume of vegetable single products, clarifies the relationship between supermarket revenue and the pricing and replenishment quantity of vegetable products based on multiple linear regression theory, and establishes a pricing strategy model based on simulated annealing algorithm and SVR. On the premise of trying to meet the market's demand for various categories of vegetable products, the supermarket will make the maximum profit, so as to help the supermarket further formulate the pricing and replenishment plan of a single product.

Open-world object detection: A solution based on reselection mechanism and feature disentanglement

Traditional object detection algorithms operate within a closed set, where the training data may not cover all real-world objects. Therefore, the issue of open-world object detection has attracted significant attention. Open-world object detection faces two major challenges: “neglecting unknown objects” and “misclassifying unknown objects as known ones.” In our study, we address these challenges by utilizing the Region Proposal Network (RPN) outputs to identify potential unknown objects with high object scores that do not overlap with ground truth annotations. We introduce the reselection mechanism, which separates unknown objects from the background. Subsequently, we employ the simulated annealing algorithm to disentangle features of unknown and known classes, guiding the detector’s learning process. Our method has improved on multiple evaluation metrics such as U-mAP, U-recall, and UDP, greatly alleviating the challenges faced by open world object detection.

Adaptive residual convolutional neural network for compressive strength prediction of energetic materials using SEM images

The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks (CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of scanning electron microscopy (SEM) images and compressive strength of polymer explosive materials. Mean absolute percentage error (MAPE) of our model is 1.3% and the goodness of fit (R2 ) is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

Optimizing Network Service Continuity with Quality-Driven Resource Migration

Despite advances in security technology, it is impractical to entirely prevent intrusion threats. Consequently, developing effective service migration strategies is crucial to maintaining the continuity of network services. Current service migration strategies initiate the migration process only upon detecting a loss of service functionality in the nodes, which increases the risk of service interruptions. Moreover, the migration decision-making process has not adequately accounted for the alignment between tasks and node resources, thereby amplifying the risk of system overload. To address these shortcomings, we introduce a Quality-Driven Resource Migration Strategy (QD-RMS). Specifically, QD-RMS initiates the migration process at an opportune moment, determined through an analysis of service quality. Subsequently, it employs a method combining Pareto optimality and the simulated annealing algorithm to identify the node most suitable for migration. This approach not only guarantees seamless service continuity but also ensures optimal resource distribution and load balancing. The experiments demonstrate that, in comparison with conventional migration strategies, QD-RMS achieves superior service quality and an approximate 20% increase in maximum task capacity. This substantiates the strategic superiority and technological advancement of the proposed strategy.

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

Collaborative vehicle-crew scheduling for multiple routes with a mixed fleet of electric and fuel buses

Since some fuel buses have not yet reached their retirement age, many cities still have a mix of fuel buses and electric buses (EBs) operating. This study focuses on multiple bus routes with fuel-electric mixed fleet. To address operation problems arising from separately scheduling vehicles and crews (known as the two-stage method) of each route, we propose a collaborative approach to optimize the vehicle dispatching plan and crew scheduling plan. Initially, we establish a nonlinear programming model by minimizing the overall operating costs of bus companies, encompassing vehicle purchase costs, EB charging costs, fuel bus refueling costs, and driver labor costs. Next, we devise an improved simulated annealing algorithm to solve the model. The proposed method is validated through a case study involving three real-world routes. A comparison with the traditional two-stage method reveals promising outcomes. Results indicate that the proposed method can decrease the number of vehicle changes by 21.43 %, driver labor costs by 2.04 %, and daily operating costs for the bus company by 0.82 %.

A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration

Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization.

Minimum mass optimization of curved grid-honeycomb sandwich panels with cutouts under buckling constraints

Frame-type stiffened structures are commonly used in engineering, the addition of the grids to the core of the sandwich panel is equivalent to adding stiffeners to the structure. The curved grids are more designable, and the load-bearing capacity can be increased through rational layout. In this paper, the curved grid-honeycomb sandwich structures with cutouts were proposed, and the optimization of the layout of the curved grids was achieved based on the minimum mass requirement under buckling constraints. A parametric model for linear buckling under uniaxial compression was established, and the parametric studies revealed that sandwich panels with curved grids had a stronger buckling resistance compared to straight grids. Combined with the Adaptive Simulated Annealing (ASA) algorithm, the layout of the curved grid was further optimal designed to solve for the minimum mass under buckling constraints, and the optimized model also included orthogonal and angle grids in engineering. The results of the optimization showed that the curved grid-honeycomb sandwich panels achieved the lightest mass, which provided the highest weight reductions of 42.76% and 24.32% compared to orthogonal and angle grids, respectively. Furthermore, the minimum number of curved grids in the optimal solution was beneficial to the manufacturing cost and structural integrity.

Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering.

Parallel Parking Path Planning and Tracking Control Based on Simulated Annealing Algorithm

Parallel Parking Path Planning and Tracking Control Based on Simulated Annealing Algorithm

An intelligent load balancing algorithm for 5G‐satellite networks

SummaryCellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed.

Optimizing software reliability growth models through simulated annealing algorithm: parameters estimation and performance analysis

Optimizing software reliability growth models through simulated annealing algorithm: parameters estimation and performance analysis