Search

The recent widespread of AI-powered real-time applications necessitates the use of edge computing for inference task offloading. Power constrained edge devices are required to balance between processing inference tasks locally or offload to edge servers. This decision is determined according to the time constraint demanded by the real-time nature of applications, and the energy constraint dictated by the device's power budget. This problem is further exacerbated in the case of systems leveraging multiple local inference models varying in size and accuracy. In this work, we tackle the problem of assigning inference models to inference tasks either using local inference models or by offloading to edge servers under time and energy constraints while maximizing the overall accuracy of the system. This problem is shown to be strongly NP-hard and therefore, we propose a hybrid genetic algorithm (HGSTO) to solve this problem. We leverage the speed of simulated annealing (SA) with the accuracy of genetic algorithms (GA) to develop a hybrid, fast and accurate algorithm compared with classic GA, SA and Particle Swarm Optimization (PSO). Experiment results show that HGSTO achieved on-par or higher accuracy than GA while resulting in significantly lower scheduling times compared to other schemes. [ABSTRACT FROM AUTHOR]

Milk-run is a cyclic material delivery system, that aims to increase the efficiency of transportation and supply chain considering the lean logistics aspect. There are two types of milk-run systems in the literature: supplier and in-plant milk-run. The in-plant milk-run system, which has attracted increasing attention with the Industry 4.0 concept, is applied to manage the delivery process of materials from the warehouse to the assembly stations in plants. This system can be implemented using Autonomous Vehicles (AV), which provide automated material handling. However, a challenging problem appears in determining milk-run routes and periods simultaneously for each AV. Furthermore, this problem becomes more difficult in the presence of assembly stations with buffer stock constraints requiring multi-commodity pickup and delivery demands. In this study, the Simulated Annealing algorithm was used due to the Np-Hard nature of the problem. Hence, we generated test instances to show the performance of the proposed algorithm. It is seen that the proposed algorithm is efficient in terms of computational times as well as determining both milk-run routes and periods. [ABSTRACT FROM AUTHOR]

Ensuring the safety of water supplies is critical for urban areas requires rapid response when water quality anomalies are detected in the pipeline network. Prompt action is essential to prevent widespread contamination, protect public health, and mitigate potential social unrest. The particle swarm optimization (PSO) algorithm has faced challenges for contamination source identification (CSI) in water distribution systems (WDS), primarily due to its susceptibility to locally optimal solutions. Addressing this issue is critical to quickly and accurately identify contamination sources. Therefore, this research integrates the Metropolis criterion from the simulated annealing (SA) algorithm into a SA-PSO algorithm, to overcome the limitations of PSO. This study conducts contamination localization experiments using SA-PSO, with the publicly available NET-3 pipeline network as the case to generate sudden contamination events. By collecting pollutant concentration data from predefined monitoring points over time through simulation, a simulation–optimization inverse location model is constructed to fit the pollutant concentrations at each monitoring point. The results of the case study show that SA-PSO outperforms PSO in both speed and accuracy in solving the CSI problem, and the findings provide an efficient and effective contamination localization tool for urban water supply management. [ABSTRACT FROM AUTHOR]

In this paper we develop a new multi-objective simulated annealing (MOSA) algorithm to generate optimal testing protocols for infectious diseases, using the COVID-19 pandemic as our context. A SEIR (susceptible-exposed-infected-recovered) epidemiological model is embedded as the computational platform for our MOSA algorithm to optimize testing protocols for screening across three joint objectives: minimum cost of test materials, minimum total infections over the testing horizon, and minimum number of false negatives over the horizon. We demonstrate the application of this optimization tool to recommend screening protocols for K-12 school districts in the U.S. State of North Carolina. Our approach is scalable by population coverage and can be employed at the level of individual school districts or regional collections of districts, individual schools or collections of schools across a district, business sites, or nursing homes, among other congregate settings where individuals may be screened prior to gaining entry to the site. The algorithm can be solved two ways, generating either independent optimal protocols across individual testing locations, or a common protocol covering all locations in the collection of testing sites. Our findings can be used to inform policy decisions to guide the development of effective testing strategies for controlling the spread of COVID-19 or other pandemic diseases in a wide range of congregate settings across various geographic regions. [ABSTRACT FROM AUTHOR]

The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk and may result in potential readmission, mortality, or revenue loss. Thus, understanding the factors that cause patients to "leave against medical advice" is vital to mitigate and potentially eliminate these adverse outcomes. This paper proposes a framework for studying the factors that affect LAMA in EDs. The framework integrates machine learning, metaheuristic optimization, and model interpretation techniques. Metaheuristic optimization is used for hyperparameter optimization-one of the main challenges of machine learning model development. Adaptive tabu simulated annealing (ATSA) metaheuristic algorithm is utilized for optimizing the parameters of extreme gradient boosting (XGB). The optimized XGB models are used to predict the LAMA outcomes for patients under treatment in ED. The designed algorithms are trained and tested using four data groups which are created using feature selection. The model with the best predictive performance is then interpreted using the SHaply Additive exPlanations (SHAP) method. The results show that best model has an area under the curve (AUC) and sensitivity of 76% and 82%, respectively. The best model was explained using SHAP method. [ABSTRACT FROM AUTHOR]

In this paper we deal with a complex real world scheduling problem closely related to the well-known Resource-Constrained Project Scheduling Problem (RCPSP). The problem concerns industrial test laboratories in which a large number of tests are performed by qualified personnel using specialised equipment, while respecting deadlines and other constraints. We present different constraint programming models and search strategies for this problem. Furthermore, we propose a Very Large Neighborhood Search approach based on our CP methods. Our models are evaluated using CP solvers and a MIP solver both on real-world test laboratory data and on a set of generated instances of different sizes based on the real-world data. Further, we compare the exact approaches with VLNS and a Simulated Annealing heuristic. We could find feasible solutions for all instances and several optimal solutions and we show that using VLNS we can improve upon the results of the other approaches. [ABSTRACT FROM AUTHOR]

Carbonfiber reinforced polymer composite (CFRP) materials are widely used in industries such as aerospace, medical equipment, and sport products. The angle between the cutting and fiber orientations significantly impacts surface quality during the machining process due to CFRP's pronounced anisotropy. The research of machinability of the fiber reinforced polymer composite (FRP) material strengthened by carbonfiber which has gained important engineering material lately will provide the utility of this material more functionally. The first way which comes to mind when the montage is mentioned is drilling of materials. In this research, the process of drilling the FRP material strengthened by carbonfiber which has such great importance have been done by producing that material in the shape of a plaque. The CFRP material (0/90° fiber orientation) has been drilled at different cutting parameters and the surface roughness of the hole has been measured. The research endeavors in the field of drilling of composite materials have focused on optimization of the operating variables, tool geometry and theoretical modeling of the surface roughness. Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. According to the results of the experimental study and optimization technique, optimum cutting parameters such as cutting speed, feed, and tool geometry were obtained in order to have the best surface quality. [ABSTRACT FROM AUTHOR]

This paper introduces the three-dimensional bin packing problem with item fragmentation (3D-BPPIF) and explores its application in the storage location assignment problem (SLAP) to efficiently allocate warehouse spaces to product groups. Based on real-world constraints, the aim is to find an effective 3D-packing of the product groups into warehouse storage spaces to minimize the total distance. Given the internal limitations present in many warehouses, the storage spaces are not homogeneous, making the allocation to product groups a challenging task that can reduce space utilization efficiency. Accordingly, to effectively utilize warehouse storage spaces, we developed a MILP formulation incorporating the concepts of shape changeability and item fragmentation, significantly enhancing the flexibility of the arrangements. Due to the NP-hard nature of the problem, we proposed a simulated annealing-based meta-heuristic to solve large-scale real-world problems. Numerous computational experiments prove the validity of the proposed model and illustrate that the proposed algorithm can provide appropriate 3D assignments. [ABSTRACT FROM AUTHOR]

Utilization of prediction and optimization techniques in machining operations assists with the decision-making required for machining parameter selection, directly impacting process outcomes including the surface roughness of the workpiece. Often these methods exclude the consideration of tool wear and critical information contained within sensor data collected during the cutting process. This study enhances the application of a hybrid physics-based and machine learning predictive framework for Ra that incorporates tool wear information via a focused investigation of computer numerical control (CNC) turning AISI 316, followed by a comparative analysis of metaheuristic optimizers to determine the optimal machining parameters. The experimental results include an analysis on the influence of the machining parameters, flank wear, and total cutting distance of the tool on the surface roughness. The proposed prediction model was improved for AISI 316 from a previous study conducted by the authors. The model achieved a root-mean-square error (RMSE) of 0.108 μm and testing results indicated that 87% of predictions fell within limits set by the ASME B46.1-2019 standard. Genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) are compared using the modified prediction model as the objective function. Despite GA producing the lowest minimum Ra for constrained and unconstrained testing cases, SA generated the highest accuracy during validation testing, achieving an error of 4.54% for a constrained scenario. The outcomes of this study strengthen the hybrid prediction framework proposed by the authors and reinforce the value the optimization process provides to machining operators in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Australian Red Cross Lifeblood collects blood from non-remunerated voluntary donors. Thus, it is important to ensure that donors experience good service so they will return to donate blood again. Donor experience is adversely influenced by prolonged waiting times, but they may be reduced by determining the staffing demand over the day. In this paper, we propose a Monte-Carlo simulation-based simulated annealing algorithm that seeks the minimum number of employees to meet demand over a single day while ensuring the system's predicted average waiting time does not exceed a specified threshold. To enhance the efficiency of our simulated annealing algorithm, we develop a novel neighbourhood search method based on the staff occupancy levels. We use data from four different Australian Red Cross Lifeblood donor centres, demonstrating that our methodology can be adapted to any donor centre to determine the minimum staffing demand. Since these staffing demands ensure the donor waiting time target is met for each donor centre, they have the potential to improve both donor and staff satisfaction as well as streamline the donor flow. [ABSTRACT FROM AUTHOR]

A Trailing Suction Hopper Dredger (TSHD) in the sailing process, excavating seabed sediment with the drag head and pumping it through the pipeline to the spoil hopper sedimentation. The spoil hopper sedimentation process, as a critical factor affecting the loading yield and efficiency of a dredger, is susceptible to the influence of different average sand grain diameter d m . However, the soil type tends to change continuously during the loading process, making it difficult to obtain the d m by measurement or calculation. In this paper, combined with the existing simplified one-dimensional sedimentation model and simulated loading data, the Bootstrap Particle Filter (BPF), Gaussian Particle Swarm Optimized Particle Filter (GPSO-PF), Assisted Particle Filter (APF), Continuous-Discrete Feedback Particle Filter based on the constant feedback gain approximation (CD-FPF-CG) and the Galerkin method feedback gain approximation (CD-FPF-GL) are applied in the numerical simulations for online estimation of important variables such as the average sand grain diameter d m , the sand bed height h s and mass m s , respectively. The simulations results show that the CD-FPF-CG has a higher numerical accuracy and efficiency than the BPF, GPSO-PF, APF and CD-FPF-GL. Therefore, the CD-FPF-CG is used to estimate the above important variables for the actual vessel and the initial value of the average sand grain diameter d m for the CD-FPF-CG is determined via Simulated Annealing Multiple Population Genetic Algorithm (SAMPGA). [ABSTRACT FROM AUTHOR]

The industrial Internet revolutionizes traditional manufacturing through the incorporation of technologies such as real-time production optimization, big data analysis, etc. Computing resource-constrained industrial terminals struggle to effectively execute latency-sensitive and computation-intensive tasks triggered by these technologies. Edge computing (EC) emerges as a promising paradigm for offloading tasks from terminals to the adjacent edge servers, offering the potentiality to augment the computational capacities for industrial terminals. However, the development of accurate offloading strategies poses a prominent challenge for EC in the industrial Internet. Incorrect offloading strategies will misguide the task offloading procedure, resulting in adverse consequences. In this paper, we study the latency-aware multi-server partial EC task offloading problem in the industrial Internet with the consideration of joining load balancing and security protection to provide accurate strategies. Firstly, we establish a task offloading model that supports partial offloading, facilitating latency reduction, task offloading across multiple edge servers with load balance, and accommodation of fuzzy task risks. We quantify the established model as a constrained optimization formulation and prove its NP-hardness. Secondly, to solve the composite offloading strategy comprising both the offloading location and offloading ratio derived from our model, we propose a bi-layer offloading algorithm with joint load balance and fuzzy security, which is based on the adaptive genetic algorithm and simulated annealing particle swarm optimization. Based on extensive experimental results, we find that the established model is effective in reducing the objective value, with a respective decrease of 27% and 46% compared to full execution in edge servers and local execution in industrial terminals. Furthermore, the proposed offloading algorithm exhibits superior performance in terms of solution accuracy compared to existing algorithms. [ABSTRACT FROM AUTHOR]

Temperature-dependent thermal conductivity of copper oxide is of great significance for the research on the thermal hazards caused by poor electrical contact. In addition, copper oxide is also a promising material in energy storage. In the aforementioned fields, the heat transfer and temperature distribution are determined by the thermophysical properties of copper oxide. However, thermal conductivity of copper oxide is seldom mentioned in the available literature. Moreover, it is impractical to test the copper oxide's thermal conductivity by the existing instruments directly due to the difficulty in sample preparation and the limitations of the equipment. Therefore, we investigate an approach to determine the temperature-dependent thermal conductivity of copper oxide using an inverse method. Temperature-drop experiments are conducted to record the heat transfer process over a broad temperature range. Three optimization algorithms, including SNOPT (Software for Large-Scale Nonlinear Programming), particle swarm optimization, and simulated annealing, except for the optimization methods, the effects of the baseline temperature and measurement errors are also tested. Results demonstrate that the particle swarm optimization is the most applicable method to solve the thermal conductivity problems with minimum errors. The average, lower and upper 95 % confidence intervals of the parameter estimation results are provided, which can be used for further heat transfer modeling. [ABSTRACT FROM AUTHOR]

The present research investigates the outcomes of inverting time domain induced polarization (TDIP) data, emphasizing the influence of parameters governing the IP response curve. In the subsurface, a resistive network follows relaxation patterns governed by the Cole-Cole model, where parameters like resistivity (ρ), chargeability (m), relaxation time (τ), and frequency exponent (c) shape the IP decay curve. Our exploration commences with exercises in forward modeling to understand how each parameter individually impacts the IP phenomenon. Subsequently, we introduce a tailored 1D forward modeling code dedicated to time domain IP, followed by a non-linear inversion process using the heat bath algorithm. These modeling equations are inherently non-linear, with the Cole-Cole parameters acting as independent variables. Using MATLAB, we implemented and rigorously validated this code using synthetic data and real field data gathered from the Sundarbans delta in West Bengal's lower deltaic region. To delve deeper into subsurface dynamics, we created contour plots resembling pseudo sections for each Cole-Cole parameter, unveiling their subsurface variations. Notably, our results demonstrate a strong correlation with the geological features of the studied area. [ABSTRACT FROM AUTHOR]

Synthesis of functional nanostructures with the least number of tests is paramount towards the propelling materials development. However, the synthesis method containing multivariable leads to high uncertainty, exhaustive attempts, and exorbitant manpower costs. Machine learning (ML) burgeons and provokes an interest in rationally designing and synthesizing materials. Here, we collect the dataset of nano-functional materials carbon dots (CDs) on synthetic parameters and optical properties. ML is applied to assist the synthesis process to enhance photoluminescence quantum yield (QY) by building the methodology named active adaptive method (AAM), including the model selection, max points screen, and experimental verification. An interactive iteration strategy is the first time considered in AAM with the constant acquisition of the furnished data by itself to perfect the model. CDs exhibit a strong red emission with QY up to 23.3% and enhancement of around 200% compared with the pristine value obtained through the AAM guidance. Furthermore, the guided CDs are applied as metal ions probes for Co2+ and Fe3+, with a concentration range of 0–120 and 0–150 µM, and their detection limits are 1.17 and 0.06 µM. Moreover, we also apply CDs for dental diagnosis and treatment using excellent optical ability. It can effectively detect early caries and treat mineralization combined with gel. The study shows that the error of experiment verification gradually decreases and QY improves double with the effective feedback loops by AAM, suggesting the great potential of utilizing ML to guide the synthesis of novel materials. Finally, the code is open-source and provided to be referenced for further investigation on the novel inorganic material prediction. [ABSTRACT FROM AUTHOR]

In the realm of reservoir development, the optimization of well placement constitutes a cornerstone challenge with significant implications that directly determine the recovery rate and economic benefits of oil and gas production. This research proposes a novel approach to optimizing well placement in reservoirs by integrating reservoir numerical simulations with intelligent optimization algorithms. The quintessence of this inquiry revolves around the strategic placing of wells amidst the complex geological fabric of reservoirs, where the objective function landscape often manifests with non-smooth, multimodal characteristics. To address those issues, the Weighted Mapping of Productivity Potential (WMPP) technique, fortified by the Simulated Annealing algorithm to judiciously ascertain specific weighting coefficients for the computation of WMPP across reservoirs is introduced in this study. Furthermore, an emblematic carbonate reservoir model serves to corroborate the adaptability and viability of WMPP for well placement optimization, underscoring its efficacy as a swift, economically viable instrument for the delineation of prospective reservoir zones and the guidance of drilling initiatives. The optimization results show that the well placement scheme guided by WMPP, which required 7 fewer wells than the oil initially in place (OOIP)-based scheme, improved 21.74% oil production over the twenty years production period. This comprehensive workflow proffers invaluable insights and benchmarks for the formulation of well placement strategies, with the proposed methodology, in its apparent simplicity, showcasing remarkable efficiency. [ABSTRACT FROM AUTHOR]

Improper waste disposal by humans has created significant environmental issues in the marine ecosystem, including endangering aquatic life and accelerating the extinction of certain marine species. Due to the floating nature of the marine debris, the coordinates for collecting activities must be estimated in advance. In this article, GNOME software is used to estimate the coordinates of debris, and then a fleet of several ships is used to collect them. Also, a mixed integer linear programming model is presented for the routing optimization of debris collection fleets. The proposed optimization model formulates the objective function based on numerous factors, including labor cost, rent, and ship insurance, and considers constraints on fuel tank capacity, the time window, and the ship's cargo capacity. A new hybrid algorithm combining the Puma algorithm and neighborhood search is proposed to address the problem. Metropolis acceptance is used in the simulated annealing algorithm to avoid the local optima and greedy selection. Numerical examples of the marine survey and the port of Rotterdam are used to test the proposed approach, which has been proven effective in several scenarios. Results achieved from the proposed hybrid method demonstrate considerable performance improvement in solving the problem. This approach has decreased total fuel and labor costs by 10–15% compared to conventional methods, with minimized time window violation reaching 25%. These results show a significant reduction in total operational costs with proper scheduling and route planning. [ABSTRACT FROM AUTHOR]

Computer security has become a growing concern for individuals, businesses, governments, and increasingly, the Internet of Things (IoT) ecosystem. Cyber-attacks can lead to significant financial losses, compromise data privacy, disrupt IoT environments, and even threaten national security. To safeguard networks, systems, and IoT devices against these risks, numerous cybersecurity techniques have been proposed. Among these, intrusion detection systems (IDS) are critical to cybersecurity strategies, enabling rapid detection, repair, and response to attacks. In this paper, we present a neural network-based intrusion detection model tailored for detecting attacks in both conventional networks and IoT environments. This model effectively identifies both known and previously unseen attacks through a robust learning phase that equips it to detect malicious activities dynamically. The model’s input features are optimized using two combinatorial optimization techniques, simulated annealing (SA) and adaptive large neighborhood search (ALNS). These techniques demonstrate high efficiency when combined with a multilayer perceptron (MLP), as reflected in the performance of our multiclass classification experiments. The effectiveness of the proposed model is evaluated using three widely studied datasets: NSL-KDD, UNSW-NB15, and CICIDS2017. Our results are competitive with state-of-the-art methods, achieving an average accuracy of 98.90% on NSL-KDD, 80.71% on UNSW-NB15, and 99.80% on CICIDS2017, showcasing the model’s potential to secure IoT networks alongside traditional environments. [ABSTRACT FROM AUTHOR]

With the IoT trend, wireless sensors are gaining growing interest. This is due to the possibility of installing them in locations inaccessible to wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize sampling to a sufficient rate to catch important information without excessive energy consumption. One way to achieve sufficient sampling is by using an algorithm for adaptive sampling named dynamic sampling rate algorithm (DSRA); however, this algorithm requires an expert to set and tune its parameters, which might not always be readily available. This study aims to further develop this algorithm to be machine learning based to tune these parameters. To achieve this goal, the algorithm was modified and an optimization strategy that considers a predetermined error threshold was developed. Then the algorithm was implemented using simulated and real data with a set of predetermined errors thresholds to observe its performance. The results showed that the developed algorithm exhibited adaptive sampling behavior, and it could collect data efficiently depending on the predetermined error threshold. Based on the results, it is possible to conclude that the developed algorithm endows sensors with adaptive sampling capabilities based on the signal rate of change. [ABSTRACT FROM AUTHOR]

With the high-speed development of the automobile industry, the number of domestic vehicles has increased significantly, which has posed a great challenge to the efficiency of road access. To fully utilize the intersection scheduling function of the intelligent transportation system, an optimization of the stranded traffic system based on LoRa is studied. Then, the greedy strategy and adaptive coefficient are used for optimization to obtain an improved genetic algorithm (GA), which is used to verify the intersection scheduling function. In addition, comparative experiments are conducted with standard GA, adaptive genetic algorithm (AGA), and hybrid genetic algorithm (HGA). The AGA optimizes the GA by adapting genetic parameters, while the HGA combines GA with a simulated annealing algorithm. The results showed that comparing the improved GA with GA, AGA, and HGA, the improved GA reduced the average value of individual extremum by 50.36%, 47.51%, and 37.16%, respectively. Compared with other mainstream algorithms, the improved GA reduced the number of stranded vehicles at intersections to 0 during traffic light timing. This indicates that the improved GA has higher and better performance and scheduling advantages in the intersection scheduling of intelligent transportation road service systems. The research aims to provide new ideas for improving traffic efficiency in intelligent transportation systems and promoting the informatization development of modern traffic management. [ABSTRACT FROM AUTHOR]

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm's accuracy and reduce the running time. To illustrate the algorithm's effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively. [ABSTRACT FROM AUTHOR]

Generating uniform design tables (UDTs) is the first step to experimenting efficiently and effectively, and is also one of the most critical steps. Thus, the construction of uniform design tables has received much attention over the past decades. This paper presents a new algorithm for constructing uniform design tables: restart discrete dynamical evolutionary algorithm (RDDE). This algorithm is based on a well-designed dynamical evolutionary algorithm and utilizes discrete rounding technology to convert continuous variables into discrete variables. Considering the optimization of UDT is a multi-objective optimization problem, RDDE uses Friedman rank to select the optimal solution with better comprehensive comparison ranking. RDDE also utilizes a simulated annealing-based restart technology to select control parameters, thereby increasing the algorithm's ability to jump out of local optima. Comparisons with state-of-the-art UDTs and two practical engineering examples are presented to verify the uniformity of the design table constructed by RDDE. Numerical results indicate that RDDE can indeed construct UDTs with excellent uniformity at different levels, factors, and runs. Especially, RDDE can flexibly construct UDTs with unequal intervals of factors that cannot be directly processed by other designs of experiment. [ABSTRACT FROM AUTHOR]

Data mining and machine learning require feature selection because features can dramatically improve model performance. In contrast, there are no polynomial solutions for selecting a subset feature. It is possible to achieve this by using meta-heuristic algorithms, specifically population-based algorithms that are able to provide a subset of features that is optimal and not exact. Meta-heuristic algorithms face challenges such as staying in local minima, easily falling into local optimum, weakly global searchability, premature convergence, and slow convergence speeds. However, recent research has limitations such as high complexity and weak initialization. In order to overcome these limitations, a three-stage model is proposed. In the first stage, the correlation of features and the correlation of features with class are considered during feature selection and used to create the initial population in the pathfinder optimization algorithm (PFA). PFA is a population-based algorithm and has some drawbacks, in the last iterations, the fluctuation rate (A) and vibration vector (ε) parameters converge to 0, and finding a new solution is impossible. As a second stage, a fuzzy inference system is designed to adjust these parameters adaptively and is called fuzzy-pathfinder optimization (FPO). In the third stage, FPO is used to select relevant features based on classification error, proportion of selected features, and redundancy. Finally, different algorithms such as simulated annealing (SA), differential evolutionary (DE), genetic algorithm (GA), particle swarm optimization (PSO), PFA, estimation of distribution algorithm (EDA), and symmetrical uncertainty criterion (SUC-PSO) are used for comparison. Based on the results, the proposed model is able to reach an average accuracy of 96% on average. Based on a comparison of the proposed algorithm with SA, DE, GA, PSO, PFA, EDA, and SUC-PSO, the objective function is improved by 17.3%, 5.6%, 3.0%, 4.5%, 5.0%, 0.5%, and 1.2%, respectively. The use of comprehensive objective functions, the adaptive adjustment of parameters, and the creation of a targeted initial population are key strengths of FPO. [ABSTRACT FROM AUTHOR]

Patient triage at emergency departments (EDs) is necessary to prioritize care for patients with critical and time-sensitive conditions. In this paper, the metaheuristic optimization algorithms simulated annealing (SA) and adaptive simulated annealing (ASA) are proposed to optimize the parameters of extreme gradient boosting (XGB) and categorical boosting (CaB). The proposed algorithms are SA-XGB, ASA-XGB, SA-CaB, and ASA-CaB. Grid search (GS), a traditional approach used for machine learning fine-tuning, is also used to fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The optimized model is used to develop an e-triage tool that can be used at EDs to predict ED patients' emergency severity index (ESI). The results show ASA-CaB outperformed all the proposed algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%, and 83.2%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes an AI-based module for a loading pattern (L/P) optimization algorithm applied to the i-SMR, designed for flexible operation. The AI module can be used as a surrogate model in the simulated annealing (SA) screening process, which allows for more efficient optimization. The convolution neural network (CNN) model was trained using reactor core L/Ps and corresponding core parameter values derived from a realistic core simulation code. For load-following operations, we selected core parameters such as control rod insertion depth, radial peaking factor, axial shape index, and effective multiplication factor. To calculate the objective function of an L/P during the SA process using core design codes, it takes approximately 3 s, while the AI-based module can predict the objective function within about 0.1 ms. During the prediction of selected parameters, we discovered two factors affecting prediction accuracy. First, the model exhibited a significant increase in error when trained on dataset containing negative values. Second, utilizing batch normalization (BN) layer and squeeze and excitation (SE) module, intended to improve accuracy, resulted in a decrease in performance of the model. Our study demonstrated that the CNN-based model achieves excellent prediction accuracy and has an ability to accelerate optimization algorithms by taking advantage of artificial intelligence's inherent computational speed. [ABSTRACT FROM AUTHOR]

Owing to low cost, high flexibility and delivery efficiency, effectively addressing the challenges of "last-mile" delivery. While collaborative truck-drone delivery systems have been proposed to overcome limitations such as limited battery life and payload capacity, they are not well-suited for large and heavy parcel delivery. To solve the issue, a pioneering heterogeneous multi-drone delivery system. In this system, the mother drone handles the delivery of large and heavy parcels, releasing small drones to manage the delivery of smaller and lighter parcels. To address the complexities of this multi-drone delivery system, we introduce a divide-and-conquer framework consisting of two integral phases. The first phase, the task allocation phase, generates multiple task allocation schemes, while the second phase, the single-drone route planning phase, produces high-quality routes for each individual drone. Two phases are performed in an iterative manner until the predefined stopping criteria are satisfied. In the task allocation phase, we propose a simulated annealing algorithm (SA) to facilitate task allocation among multiple drones, utilizing transfer and recombination operators to generate high-quality solutions. After obtaining the task allocation scheme, a satisfactory route of a mother drone is generated by a variable neighborhood descent algorithm (VND). A desirable route for each single small drone is produced by dynamic programming (DP).Extensive experiments are conducted, demonstrating the outstanding optimization and time efficiency of the proposed two-phase optimization method by the fact that it obtains within a 4.89% gap from the optimal solution generated by CPLEX in 15.48 s for instance up to 125 nodes. [ABSTRACT FROM AUTHOR]

The non-waiting constraint is a crucial factor in industries with continuous production flows. Recognizing the significance of this constraint in the manufacturing process, we propose several approaches to minimize the maximum tardiness in the no-wait manufacturing flow shop scheduling problem. This research presents two main approaches: an exact method that utilizes mixed integer linear programming and an approximate method based on constructive heuristics and hybrid meta-heuristics. To tackle the problem, we introduce multiple heuristics and hybrid improved meta-heuristics based Nawaz Enscore Ham, greedy randomized adaptive search procedure and genetic algorithm. Furthermore, we propose three hybrid meta-heuristics based on the simulated annealing approach. To validate the effectiveness and robustness of our methods, we conducted experiments on multiple instances of the flow shop problem proposed by Taillard. The results demonstrate that the hybrid algorithm, which combines a greedy randomized adaptive search procedure with the insertion procedure and simulated annealing, exhibits strong performance. In fact, this algorithm achieved a success rate of 72 % across 200 test instances. It outperformed the other two meta-heuristics, with a minimum average relative percentage deviation of only 0.0039 % . [ABSTRACT FROM AUTHOR]

In polymer membrane fuel cells (PEMFC), the pore microstructure and the effective diffusion coefficient ( D eff ) of the catalytic layer have a significant impact on the overall performance of the fuel cell. In this work, numerical methods to simulate PEMFC catalytic layers were used to study the effect of isotropy ( I xy ) on the D eff . The proposed methodology studies reconstructed systems by Simulated Annealing imaging with different surface fractions of microstructures composed by two diffusive phases: agglomerates and pores. The D eff is determined numerically by the Finite Volume Method solved for Fick's First Law of Diffusion. The results show that the proposed methodology can effectively quantify the effect of isotropy on the D eff for both diffusion phases. Two trends were obtained in the magnitude of the D eff concerning the change in isotropy: (1) an analytical equation is proposed in this article for D eff ≥ 5 % D 0 and (2) numerical solutions are determined for D eff < 5 % D 0. In our analytical equation are both a lineal and a logarithmic sweep. When the surface fraction is ∅ = 50%, the D eff decreases more linearly than ∅ = 10 % at the beginning of the isotropy change, which indicates that small changes in isotropy in the particulate material modify it drastically; under these conditions the diffusion coefficient in the pore is predominant. (3) When the surface fraction is less than 50%, the D eff decreases more exponentially at the beginning and more linearly at the end of the isotropy change, which shows that small isotropy changes in the bar-aligned material drastically alter it. In this trend, diffusion in the agglomerate is less affected by isotropy. The proposed methodology can be used as a design tool to improve the mass transport in porous PEMFC electrodes. [ABSTRACT FROM AUTHOR]

Supply or service networks are vulnerable to hazards that can stem from both unintentional and intentional human actions, as well as natural calamities. To ensure vital infrastructure resilience in these networks, address the interdiction facility location problem. Currently, diverse groups of attackers target supply and service systems to cause maximum disruption. Collaboration among attackers improves system vulnerability detection accuracy and realism. This research examines the challenges of interdiction location with different defense systems and heterogeneous attackers. To address this challenge, a mixed-integer non-linear bi-level programming model was considered. Heuristic optimization methods including variable neighborhood search, simulated annealing, and hybrid variable neighborhood search are used to efficiently solve the suggested model. The outcomes of our investigation suggest that implementing various protective methods leads to an escalation in system damage when attackers collaborate. Furthermore, the findings illustrate the efficacy of the suggested algorithms in resolving interdiction location issues within supply or service networks. [ABSTRACT FROM AUTHOR]

Software testing is an effective means of ensuring software quality. The cost of software testing is the main component of the total cost of software development. The generation of test data is very important in testing, because the efficiency of testing depends on the test data used. A significant part of software testing automation is the test data generation. How to automatically generate test datasets is still an open problem. Search-based software testing (SBST) is a process of generating test data that employs meta-heuristic methods to resolve tough NP-hard problems. The test data generation problem belongs to NP-hard problems. This paper focuses on the automatic generation of test data for path coverage based on control flow criteria in structural testing. Some meta-heuristic algorithms have been used to search for software test data. To advance the efficiency and effectiveness of path test data generation, an adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed. The probabilistic jumping property of simulated annealing (SA) algorithm is introduced into the particle swarm optimization (PSO) algorithm to make the particle accept bad solution at a certain probability during the searching process, and therefore boost the capacity of the proposed algorithm to bounce from the local optimum. The fitness function is constructed by the branch function superposition to properly guide the search process. In addition, to improve the convergence speed of the algorithm, an adaptive adjustment scheme based on inertial weight and an adjustment scheme based on learning factor are proposed as well. The experimental results show that the proposed method can effectually avoid premature convergence and effectively upgrades the efficiency of generating test data automatically. It is also competitive in solving other complicated optimization problems. [ABSTRACT FROM AUTHOR]

The traveling salesperson problem (TSP) is one of the most iconic hard optimization tasks. With frequency fitness assignment (FFA), a new approach to optimization has recently been proposed: instead of directing the search towards better solutions, the optimization process prefers those with rarely encountered objective values. FFA can be plugged into existing algorithms and can improve their performance on some problems that are hard for them. However, problems like the TSP, where the number of possible objective values (here: tour lengths) is high, cause the performance of FFA-based algorithms to deteriorate. We choose 56 symmetric instances from TSPLib as a testbed for our experiments. We plug FFA both into the (1+1) EA and simulated annealing (SA) and obtain the FEA and FSA, respectively. We find that the resulting FEA substantially outperforms the (1+1) EA and that FSA can reach the global optima more often than SA within our computational budget. We propose two ways to hybridize FFA and traditional optimization in a round-robin scheme where objective function evaluations are alternatingly assigned to both components. The FFA-based parts of the algorithms sometimes send a solution over to the traditional-optimization-based ones. This can lead to further performance improvements. In particular, the hybrids combining simulated annealing with the FEA solve most problems and find close-to-optimal solutions on other instances. Even for several instances with many possible different objective values, which are therefore not suited to FFA, hybrids combining FFA and traditional objective-guided optimization algorithms can offer substantial performance improvements. [ABSTRACT FROM AUTHOR]

Quantum computing offers a novel perspective for solving classically intractable problems. The computational heuristic non-binary nature of quantum computers, based on quantum mechanical properties, associates several advantages and disadvantages. We explore quantum annealing (QA), a metaheuristic algorithm with the primary objective of solving NP-hard optimization problems intractable to classical algorithms. Here we solve a particular family of NP problems, called origin–destination integer multi-commodity flow problem (ODIMCF), using QA. This includes the quadratic unconstrained binary optimization (QUBO) formulation and the practical implementation of this problem on the D-Wave QA. Additionally, quantum annealers are benchmarked against other classical solvers to assess the ODIMCF problem's solution quality and runtime performance. [ABSTRACT FROM AUTHOR]

In the generalized continuum mechanics (GCM) theory framework, asymmetric wave equations encompass the characteristic scale parameters of the medium, accounting for microstructure interactions. This study integrates two theoretical branches of the GCM, the modified couple stress theory (M-CST) and the one-parameter second-strain-gradient theory, to form a novel asymmetric wave equation in a unified framework. Numerical modeling of the asymmetric wave equation in a unified framework accurately describes subsurface structures with vital implications for subsequent seismic wave inversion and imaging endeavors. However, employing finite-difference (FD) methods for numerical modeling may introduce numerical dispersion, adversely affecting the accuracy of numerical modeling. The design of an optimal FD operator is crucial for enhancing the accuracy of numerical modeling and emphasizing the scale effects. Therefore, this study devises a hybrid scheme called the dung beetle optimization (DBO) algorithm with a simulated annealing (SA) algorithm, denoted as the SA-based hybrid DBO (SDBO) algorithm. An FD operator optimization method under the SDBO algorithm was developed and applied to the numerical modeling of asymmetric wave equations in a unified framework. Integrating the DBO and SA algorithms mitigates the risk of convergence to a local extreme. The numerical dispersion outcomes underscore that the proposed SDBO algorithm yields FD operators with precision errors constrained to 0.5‱ while encompassing a broader spectrum coverage. This result confirms the efficacy of the SDBO algorithm. Ultimately, the numerical modeling results demonstrate that the new FD method based on the SDBO algorithm effectively suppresses numerical dispersion and enhances the accuracy of elastic wave numerical modeling, thereby accentuating scale effects. This result is significant for extracting wavefield perturbations induced by complex microstructures in the medium and the analysis of scale effects. [ABSTRACT FROM AUTHOR]

Porosity and acoustic impedance are important in the study of subsurface properties of rocks and soil. Porosity is influenced by the type of minerals, and fluids, and their distribution within the subsurface material. Acoustic impedance is a key parameter in seismic inversion because it governs the reflection and transmission of seismic waves at interfaces between different rock layers. Mapping porosity and acoustic impedance using seismic inversion poses several challenges such as low resolution, longer convergence times compared to other optimization techniques, and handling large datasets. To address these challenges, our current study has employed a semi-hybrid optimization approach by incorporating a pattern search (PS) method into the globally recognized simulated annealing (SA) technique. In our devised methodology, seismic data is meticulously inverted, trace by trace, initially utilizing the simulated annealing process and subsequently integrating the pattern search which further reduces computational Complexity. The output from SA serves as the foundation for the PS optimization, preventing it from getting trapped in local minima or maxima. To evaluate the algorithm, we initiated a systematic analysis using synthetic data. The hybrid optimization method performed well, yielding highly accurate inversion results with a remarkable high resolution and correlation between original and inverted impedance. We then applied this approach to actual seismic reflection data from the Blackfoot field in Alberta, Canada. Notably, the inversion identified a sand channel between 1055 and 1070 ms two-way travel time, characterized by low impedance and high porosity, suggesting the potential presence of hydrocarbon reservoirs. The level of performance demonstrated in this context may not be anticipated when utilizing SA or PS optimization alone. Hence, the newly devised semi-hybrid optimization approach emerges as a highly recommended solution, offering the potential to address the constraints of individual optimization methods and deliver thorough subsurface insights. [ABSTRACT FROM AUTHOR]

The allocation of resources and scheduling of tasks, specifically mapping, in multicore systems on-chip (MCSoC), poses significant challenges. Tasks have diverse resource requirements and interact with each other, while network-on-chip (NoC)-based MCSoC consists of heterogeneous cores and communication networks. The heterogeneity of resources in MCSoC, along with the varying computational and communication demands of applications, makes mapping a complex optimization problem. We have mathematically modeled the mapping problem in NoC-based MCSoC using mixed-integer linear programming (MILP) with the objective of minimizing the execution time of applications. This model incorporates computation and communication capacity, energy budget constraints of MCSoC, and execution time requirements of applications. We further propose heuristics, including simulated annealing (SA) and genetic algorithm (GA), considering the capacity and budget constraints of MCSoC systems to accelerate applications and provide quick mapping solutions. Simulation results demonstrate that the performance from SA and GA heuristics is very close (within 10%) to the optimal solutions from MILP across various applications for 2D-Mesh NoCs with 16–100 cores. The energy consumption of SA and GA heuristics is also very close to the optimal solutions from MILP, with a few exceptions on small-scale 16-core NoC. Additionally, SA outperforms GA in most cases for all applications. [ABSTRACT FROM AUTHOR]

The interpretation of the CHAMP and Swarm-A satellites magnetic anomalies is discussed. The vertical gradient of the Swarm-A anomalies is presented. The vertical gradient shows in detail the magnetic sources. The inversion of the CHAMP magnetic anomalies is also presented. The inversion shows the parameters of the selected forward model. The inversion is solved by numerical procedures: the nonlinear Simplex and Simulated Annealing methods. Possible origin of upper crust magnetization is summarized. The calculation of the error propagation is estimated. [ABSTRACT FROM AUTHOR]

Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence and widespread occurrence of diverse forms of ransomware present a significant impediment to the pursuit of reliable security measures that can effectively combat them. This constitutes a formidable challenge due to the dynamic nature of ransomware, which renders traditional security protocols inadequate, as they might have a high false alarm rate and exert significant processing demands on mobile devices that are restricted by limited battery life, CPU, and memory. This paper proposes a novel intelligent method for detecting ransomware that is based on a hybrid multi-solution binary JAYA algorithm with a single-solution simulated annealing (SA). The primary objective is to leverage the exploitation power of SA in supporting the exploration power of the binary JAYA algorithm. This approach results in a better balance between global and local search milestones. The empirical results of our research demonstrate the superiority of the proposed SMO-BJAYA-SA-SVM method over other algorithms based on the evaluation measures used. The proposed method achieved an accuracy rate of 98.7%, a precision of 98.6%, a recall of 98.7%, and an F1 score of 98.6%. Therefore, we believe that our approach is an effective method for detecting ransomware on portable devices. It has the potential to provide a more reliable and efficient solution to this growing security threat. [ABSTRACT FROM AUTHOR]

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, range, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-s) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a two-step validation process using multiple linear regression models, model fitness (adjusted R2) and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability. [ABSTRACT FROM AUTHOR]

To address the issues of curvature discontinuity and terminal tire non-return in the parallel parking of autonomous vehicles, a novel parallel parking path planning method based on the combination of the quintic polynomial curve and an improved sigmoid function was proposed. First, a vehicle kinematic model was established. Second, considering the position, front wheel angle, and yaw angle constraints during the parking process, a hybrid superimposed curve was designed. The parking path planning problem was converted into an optimal control problem, with the maximum curvature and curvature at both ends as objective functions, and the parameters were optimized using the simulated annealing algorithm. Subsequently, for path tracking control, the simulated annealing algorithm was used to optimize the prediction time horizon of the model predictive control algorithm. Finally, a series of actual vehicle experiments were conducted based on the Apollo Autonomous Driving Developer Suite, and the effectiveness of the proposed path planning method was validated. Therefore, this method can provide a certain reference for automatic parking path planning technology. [ABSTRACT FROM AUTHOR]

In vehicular cloud computing (VCC) systems, the vehicle cloud (VC) consists of computing resources for multiple vehicles to assist the remote cloud (RC) in making real-time decisions on driving vehicles, to improve road safety and driving comfort. The acquisition of additional cloud facilities to increase the capacity of VCC systems when processing intensive requests for cloud resources is costly. A recent approach uses multiple remote microclouds (RMCs) to assist RCs in processing requests because they are smaller and less costly than RCs. Therefore, this paper presents a VCC system with multiple RCs and RMCs, in which an algorithm for allocating request resources among VCs, RMCs, and RCs. Subsequently, this paper proposes an improved simulated annealing algorithm (ImSA) based on density of vehicles in a geographical area to deploy RCs and RMCs with the minimal deployment cost and response cost. For real applications, the accuracy and effectiveness of the proposed algorithm are evaluated on the basis of the real traffic data from three different areas (i.e., urban area, outskirts, and suburban area) in one week. [ABSTRACT FROM AUTHOR]

Maintenance planning aims to improve the reliability of assets, prevent the occurrence of asset failures, and reduce maintenance costs associated with downtime of assets and maintenance resources (such as spare parts and workforce). Thus, effective maintenance planning is instrumental in ensuring high asset availability with the minimum cost. Nevertheless, to find such optimal planning is a nontrivial task due to the (i) complex and usually nonlinear inter-relationship between different planning decisions (e.g., inventory level and workforce capacity), and (ii) stochastic nature of the system (e.g., random failures of parts installed in assets). To alleviate these challenges, we study a joint maintenance planning problem by considering several decisions simultaneously, including workforce planning, workforce training, and spare parts inventory management. We develop a hybrid solution algorithm (DRLSA ) that is a combination of Double Deep Q-Network based Deep Reinforcement Learning (DRL) and Simulated Annealing (SA) algorithms. In each episode of the proposed algorithm, the best solution found by DRL is delivered to SA to be used as an initial solution, and the best solution of SA is delivered to DRL to be used as the initial state. Different from the traditional SA algorithms where neighborhood structures are selected only randomly, the DRL part of DRLSA learns to choose the best neighborhood structure to use based on experience gained from previous episodes. We compare the performance of the proposed solution algorithm with several well-known meta-heuristic algorithms, including Simulated Annealing, Genetic Algorithm (GA), and Variable Neighborhood Search (VNS). Further, we also develop a Machine Learning (ML) algorithm (i.e., K-Median) as another benchmark in which different properties of spare parts (e.g., failure rates, holding costs, and repair rates) are used as clustering features for the ML algorithm. Our study reveals that the DRLSA finds the optimal solutions for relatively small-size instances, and it has the potential to outperform traditional meta-heuristic and ML algorithms. [ABSTRACT FROM AUTHOR]

Step error is the machining error between adjacent cutter location points in the feeding direction. In order to improve the computational efficiency, a hybrid particle swarm optimization method (HPSO) combining genetic algorithm (GA) and simulated annealing (SA) algorithm is proposed. The mapping relationship between local cutter contact (CC) curve in step error calculation and the particle search range and the fitness calculation model are established. The maximum fitness value is taken as step error. The chaotic initialization population is carried out by the Tent mapping. Two nonlinear control methods based on the Sigmoid function and the numbers of iterations are proposed for inertia weight and learning factors, respectively. Combined with the above optimizations, an improved particle swarm optimization algorithm (IPSO) is proposed algorithm is formed. Based on IPSO, the crossover and mutation strategies of GA are used to increase particle diversity, and then, Metropolis criterion from SA is applied to the particle selection; the improved crossover and mutation particle swarm optimization algorithm (ICMPSO) is formed. IPSO is used for Elite particles with higher fitness values to enhance the convergence speed. The other Ordinary particles employ ICMPSO to improve global search capability. The combination of IPSO and ICMPSO forms a whole hybrid particle swarm optimization (HPSO) method. All the proposed algorithms are implemented, and two typical free-form surfaces are taken as examples to calculate step errors. The calculation results show that the tool path generation time of the proposed method is lower than that of the geometric iterative algorithm and the standard particle swarm optimization algorithm, which verifies the feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we study the m-machine ( m ≥ 3 ) permutation flow shop scheduling in which the processing time of a job depends not only on the sum of actual processing times of previous jobs but also on its job-dependent truncation parameter, i.e., the general truncated learning effects. The goal is to find a sequence so as to minimize the makespan, we prove that this problem is NP-hard. Two simple heuristics are proposed and their worst-case bounds are presented. We also propose a branch-and-bound algorithm and some heuristics to solve this problem. Further, computational experiments are conducted to determine the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices. [ABSTRACT FROM AUTHOR]

As a main carrier mode for the sustainable development of the construction industry in China, prefabricated building may lead to problems such as cost overruns, project delays, and waste of resources due to unreasonable selection of prefabricated components. Therefore, we quantitatively analyze the contribution rate of quality optimization of prefabricated components using QFD-SEM. Under the constraints of prefabrication rate, quality optimization contribution rate, and expected values of various sub-goals, we propose a multi-objective optimization method for prefabricated component combinations based on cost, duration, and carbon emissions. By using NSGA-II to solve the model, we can obtain a set of optimal Pareto solutions for prefabricated component combinations. Based on the optimal Pareto solution set, we establish a multi-objective evaluation model using simulated annealing optimization projection tracing method, and select the optimal prefabricated component combination solution according to the projected eigenvalues of the solutions. An empirical study is conducted using an eleven-story framed building in Shenzhen, Guangdong Province, China as a case study. The results show that: (1) Using this method, optimal solutions can be obtained in an unbounded solution space, with the optimal solution having advantages over both fully cast-in-place and fully prefabricated solutions. Compared to the fully cast-in-place solution, the duration and carbon emissions are reduced by 36.62% and 12.74% respectively, while compared to the fully prefabricated solution, costs are reduced by 4.15%. (2) There is a certain negative correlation between the cost of prefabricated component combinations and duration, carbon emissions, and quality optimization, while there is a certain positive correlation with the prefabrication rate. (3) The size of the optimal projection direction vector based on the optimization objectives indicates that carbon emissions have the greatest impact on the evaluation results of the solutions. [ABSTRACT FROM AUTHOR]

Two classes of quantum-annealing-inspired-algorithms (QAIA), namely different variants of simulated coherent Ising machine and simulated bifurcation, have been proposed for efficiently solving combinatorial optimization problems recently. In order to certify the superiority of these algorithms, standardized comparisons among them and against other physics-based algorithms are necessary. In this work, for Max-Cut problems up to 20,000 nodes, we benchmark QAIA against quantum annealing and other physics-based algorithms. We found that ballistic simulated bifurcation excelled for chimera and small-scale graphs, achieving nearly a 50-fold reduction in time-to-solution compared to quantum annealing. For large-scale graphs, discrete simulated bifurcation achieves the lowest time-to-target and outperforms D-Wave Advantage system when tasked with finding the maximum cut value in pegasus graphs. Our results suggest that QAIA represents a promising means for solving combinatorial optimization problems in practice, and can act as a natural baseline for competing quantum algorithms. One of the most considered applications for quantum-inspired algorithms is solving combinatorial optimization problems. In this manuscript, the authors benchmark several quantum-annealing-inspired algorithms against other state-of-the-art optimizers and quantum annealer, comparing their success probabilities and time-to-solution. [ABSTRACT FROM AUTHOR]

The 2D mesh topology-based Network-on-Chip (NoC) is a prevalent structure in System-on-Chip (SoC) designs, offering implementation and fabrication benefits. However, increased NoC scale leads to longer communication paths, more hops, and higher end-to-end latency and energy consumption. To mitigate these issues, Wireless NoC (WiNoC) integrates wireless communication, enhancing data rates, energy efficiency, and routing flexibility. Despite several mapping algorithms for NoCs, optimal techniques for hybrid WiNoCs are underexplored. This study proposes two novel application mapping methods for 2D mesh topology-based hybrid WiNoCs, using quadratic programming (QP) and simulated annealing (SA). Our goal is to minimize communication-related energy consumption. We evaluated these methods across various wireless router configurations, benchmarks, and custom application graphs. The QP-based method excels in smaller problems, while the SA-based approach yields optimal or near-optimal results for larger sizes within practical runtimes. [ABSTRACT FROM AUTHOR]

In artificial intelligence (AI), optimization techniques are used to solve several problems in different fields. One of these areas is software reliability verification, which is an important part of software products, as it helps determine how reliable the software is to complete its functions. This is done by estimating the parameters of software reliability growth models (SRGMs). SRGMs predict the expected number of failures after completion, while also serving as an indicator of software readiness for delivery. Therefore, this study aims to optimize the estimation of these parameters based on the available failure data using one of the stochastic optimization algorithms, the simulated annealing algorithm (SA) due to its power and effectiveness. Three SRGMs' models are studied: delayed S-shaped, Musa-Okumoto logarithmic and Power models, to examine the feasibility of the proposed algorithm using five different data sets. The results were compared and analyzed with several algorithms: Particle swarm optimization (PSO), cuckoo search (CS), modify whale optimization algorithm (MWOA), S-shaped model with logistic TEF and social spider algorithm (SSA). A comparison was also made with recent SRGMs that do not rely on AI techniques. The results showed that the proposed algorithm based on SA outperformed all other methods in finding the optimal parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Traditional network interdiction problems focus on removing vertices or edges from a network so as to disconnect or lengthen paths in the network; network diversion problems seek to remove vertices or edges to reroute flow through a designated critical vertex or edge. We introduce the all-pairs vitality maximization problem (VIMAX), in which vertex deletion attempts to maximize the amount of flow passing through a critical vertex, measured as the all-pairs vitality of the vertex. The assumption in this problem is that in a network for which the structure is known but the physical locations of vertices may not be known (e.g., a social network), locating a person or asset of interest might require the ability to detect a sufficient amount of flow (e.g., communications or financial transactions) passing through the corresponding vertex in the network. We formulate VIMAX as a mixed integer program, and show that it is NP-Hard. We compare the performance of the MIP and a simulated annealing heuristic on both real and simulated data sets and highlight the potential increase in vitality of key vertices that can be attained by subset removal. We also present graph theoretic results that can be used to narrow the set of vertices to consider for removal. [ABSTRACT FROM AUTHOR]