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This study addresses a practical problem within a multi-level supply chain where a wide range of customers can be served through different strategies such as make-to-stock, make-to-order, or vendor-managed inventory. The customer demand is stochastic, and sensitive to pricing associated with different production-inventory strategies. We propose a two-stage stochastic mixed-integer non-linear programming model. In the first stage, decisions are made regarding the selection of production-inventory strategies and pricing to maximise the expected profit. The second stage involves decisions related to production, inventory, and distribution, which are used to evaluate the first-stage decisions under various scenarios with different levels of accuracy. To solve the model, a metaheuristic approach based on the Simulated Annealing algorithm is developed. To showcase the practical applicability of our model and solution approach, we use a real case study in a Canadian pulp and paper supply chain. The results revealed that both the production-inventory strategy assigned to customers and the sales price underwent changes across scenarios. Furthermore, we demonstrated that by implementing the SA algorithm, we could improve the initial profit by up to 1.43% through slight adjustments in the sales price and assigned strategies for customers. [ABSTRACT FROM AUTHOR]

In the Beyond Moore era, QCA (Quantum-dot Cellular Automata) has been widely studied because of its low power consumption and high device concentration. As the core of chip design, EDA technology has become an indispensable step in QCA research. It refers to the use of computer-aided design software to complete large-scale circuit function design, logic synthesis, placement and routing, design rule check and other processes. In the present study, there are still many defects in QCA placement and routing. The main problems it faces are low success rate, too large area and too many cross lines. To address these problems, our team had proposed a hybrid strategy based on the genetic algorithm and the improved A-star algorithm. But the running time of the previously proposed algorithm is not satisfying, besides it lacks self-adaption feature which is important as the problem size varies among different circuits. This paper replaces the genetic algorithm with a simulated annealing algorithm, and introduces a self-adaption strategy to better accommodate different circuit sizes. Experimental results show that the new algorithm combination possesses a higher success rate, lower running time and more intelligent adaption ability. [ABSTRACT FROM AUTHOR]

This paper concerns a real-life production management problem composed of two stages, which includes interdependent batch scheduling problems. Moreover, both stages require setup operations at the beginning and between the subsequent batches. From the optimisation point of view, this production management problem is complying with customers' deadlines and hence minimising total lateness. To achieve this objective, the production planner must identify optimum or near-optimum batch schedules for both stages. Correspondingly, this paper aims to develop a methodology to manage the related production problem as accurately as possible. We formulate an optimisation model that employs the mathematical programming method in line with this. Afterwards, an algorithmic proposal based on the simulated annealing algorithm is also developed to solve the problem in realistic sizes. The computational capabilities of the developed model and the algorithm are evaluated on the randomly generated problem sets. A direct comparison between the mathematical model and the algorithm shows how efficiently the proposed algorithm solves real-world problems. Also, computational results indicate that the proposed algorithm satisfactorily solves the related real-world batch scheduling problem. [ABSTRACT FROM AUTHOR]

In the process of natural gas hydrate exploitation, the secondary hydrate formation may occur in both the reservoir and the completion layer. The secondary hydrate formed during the exploitation process and the sand particles falling from the reservoir will block the flow channel and reduce the gas production. In this paper, the permeability change of sand filling layer under the influence of sand and hydrate blockage is studied from a three-dimensional perspective, and the permeability change characteristics of sand mixing and secondary hydrate formation in filling layer caused by pore space blockage are summarized. A three-dimensional model for predicting the permeability of gravel layer is developed. In previous studies, the mathematical model parameters derived from the empirical formula are always not unique, which will cause inaccurate permeability prediction results. In this study, the simulated annealing algorithm is used to optimize the parameters such as boundary conditions and cooling tables. The undetermined parameters in the existing mathematical prediction model of gravel layer permeability are optimized into a determined maximum value to solve the optimization problem of the undetermined parameters of the permeability model. The algorithm in this paper takes the actual value of gravel layer permeability with different sand content and hydrate concentration in the experiment as the standard, and takes the mean square error of the predicted value and the experimental value of the prediction model as the objective function. The simulated annealing algorithm is used to estimate and optimize the undetermined parameters of the model. The algorithm results show that the mean square error between the predicted value and the experimental value is minimized with n = 22.7 and τ = 1.014(n and τ are the parameters to be optimized in the permeability model of this paper). The fitting degree between the optimized permeability prediction model and the experimental value is higher than that before optimization, which verifies the correctness and rationality of the algorithm in this paper. • Simulated annealing algorithm is used to optimize the permeability model. • The simulated annealing algorithm is optimized to have faster solution speed. • The NMSE error calculation method is used to make the predicted permeability model better fit the actual value. • The optimized model is combined with the capacity model. [ABSTRACT FROM AUTHOR]

In view of the problems of high cost and complicated parameters, the magnetic field simulation model of 100 mm cold crucible experimental device is established by COMSOL simulation software, and the simulation experiments of high-frequency power supply and coil parameters are carried out. The optimal combination of high-frequency power supply parameters is predicted by neural network and simulated annealing algorithm. The magnetic field distribution, eddy current loss and energy utilization of cold crucible are simulated under different current intensity, frequency, coil height, turns, diameter and spacing. Based on the above simulation results, the neural network can quickly predict the magnetic field and eddy current loss under various power supply parameters, and finally use the simulated annealing algorithm to predict the optimal combination of power supply parameters. The simulation results show that after parameter optimization, the magnetic field uniformity can be greatly improved, the melting effect can be improved, the energy utilization rate can be increased by 68%, and energy waste can be avoided. These results preliminarily verify the improvement effect of high-frequency power supply and coil parameter optimization on magnetic field distribution and energy use efficiency, which can provide an important basis for the optimization design of power supply parameters and coil parameters of the following cold crucible engineering prototype. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is a well-known discrete optimization problem that has an impact on theoretical and practical applications. In this paper, a freight distribution model that includes a charging system located at the depot, making it feasible for real world-implementation, is proposed. Two different solution methods are proposed and compared: a genetic algorithm (GA) and a population-based simulated annealing (PBSA) with the number of moves increasing during the iterations. Among the variety of algorithm used to solve the VRP, population-based search methods are the most useful, due to the ability to update the memory at each iteration. To demonstrate the practical aspects of the proposed solution a case study is solved using travel time on a real network to evaluate the potentiality for a real-world application. [ABSTRACT FROM AUTHOR]

This paper mainly studies the collaborative operation of a cross-border e-commerce supply chain composed of manufacturers, e-commerce platforms and foreign warehouses. Firstly, the decision models of transnational e-commerce enterprises based on decentralized, centralized, and hybrid modes are established. The sensitivity of the contract and each determining variable is analyzed using the simulation method. Through the joint contract of "revenue sharing + volume discount," the production efficiency of the enterprise and the logistics service of the international warehouse can be improved. The genetic algorithm combined with simulated annealing was adopted. Finally, a concrete example is given to verify the feasibility of the proposed method. The results show that the member departments can work together better under centralized decision-making compared to the decentralized management mode. Manufacturers to increase production capacity and improve the level of logistics services in overseas warehouses will help improve the profitability of cross-border e-commerce. [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]

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers' risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses. [ABSTRACT FROM AUTHOR]

In the drilling process of printed circuit boards, achieving the operation with the least cost depends on the distance travelled by the drilling route. In this study, an integrated method is proposed to carry out the drilling process with the least cost and high speed. In the proposed method, the positions of electronic circuit elements to be placed on the printed circuit board are detected using image processing techniques on a two-dimensional circuit image. Annealing Simulation, Particle Swarm Optimization and Ant Colony Algorithms have been used to solve the travelling salesman problem, and their performances have been examined. When comparing the test results of the obtained models, it is observed that the Ant Colony Algorithm, at the same iteration values, provides solutions with significantly shorter route lengths compared to Simulated Annealing and Particle Swarm Optimization algorithms. [ABSTRACT FROM AUTHOR]

AbstractIn this article, the parameter estimation of several commonly used two-parameter lifetime distributions, including the Weibull, inverse Gaussian, and Birnbaum–Saunders distributions, based on joint progressively Type-II right-censored sample is studied. Different numerical methods and algorithms are used to compute the maximum likelihood estimates of the unknown model parameters. These methods include the Newton–Raphson method, the stochastic expectation–maximization (SEM) algorithm, and the dual annealing (DA) algorithm. These estimation methods are compared in terms of accuracy (e.g. the bias and mean squared error), computational time and effort (e.g. the required number of iterations), the ability to obtain the largest value of the likelihood, and convergence issues by means of a Monte Carlo simulation study. Recommendations are made based on the simulated results. A real data set is analyzed for illustrative purposes. These methods are implemented in Python, and the computer programs are available from the authors upon request. [ABSTRACT FROM AUTHOR]

This study addresses the set orienteering problem with mandatory visits (SOPMV), a variant of the team orienteering problem (SOP). In SOPMV, certain critical sets must be visited. The study began by formulating the mathematical model for SOPMV. To tackle the challenge of obtaining a feasible route within time constraints using the original MILP approach, a two-stage mixed-integer linear programming (MILP) model is proposed. Subsequently, a simulated annealing (SA) algorithm and a dynamic programming method were employed to identify the optimal route. The proposed SA algorithm was used to solve the SOP and was compared to other algorithms, demonstrating its effectiveness. The SA was then applied to solve the SOPMV problem. The results indicate that the solutions obtained using SA are superior and more efficient compared to those derived from the original MILP and the two-stage MILP. Additionally, the results reveal that the solution quality deteriorates as the ratio of the set of mandatory visits increases or the maximum allowable travel time decreases. This study represents the first attempt to integrate mandatory visits into SOP, thereby establishing a new research direction in this area. The potential impact of this research is significant, as it introduces new possibilities for addressing complex combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

Due to the low signal-to-noise ratio of raw data, the reliability of data and the accuracy of acoustic source localization are severely affected by fiber optic acoustic sensing technology. To address this issue, this study optimizes the wavelet thresholding method. Firstly, a novel thresholding function is proposed, which achieves denoising while preserving key information through shape adjustment factors. It combines the advantages of both hard and soft threshold functions and has high flexibility and controllability. Secondly, an adaptive threshold calculation method is introduced, utilizing an improved simulated annealing algorithm to optimize threshold selection, reducing the algorithm’s dependence on threshold parameter selection. Through simulation experiments, it has been verified that this research method effectively suppresses noise in the signal and improves data availability. Compared to the original methods, this approach significantly improves the signal-to-noise ratio and demonstrates robustness in simulated tests of real signals. [ABSTRACT FROM AUTHOR]

Accurate analysis of creep effects in finite element analysis depends on whether the exponential form of the creep coefficient can accurately reproduce the creep coefficient curve. This paper aims to develop a high-precision fitting method for arbitrary creep coefficient curves to achieve accurate finite element creep analysis. The core issue was examined, and an optimization algorithm was proposed. First, the simulated annealing algorithm was used to search for the global optimal solution, followed by the application of the Levenberg-Marquardt algorithm for precise calculations. Two examples were employed to verify the method. The results showed that: (1) The proposed creep coefficient curve fitting method could accurately fit the creep coefficient curve. (2) The case studies showed that the algorithm achieved a correlation coefficient ( R) above 0. 999 9 when fitting the creep coefficient curves according to highway and railway standards, indicating high fitting accuracy and strong general applicability. (3) The proposed algorithm featured a simple structure and clear hierarchy, allowing for easy implementation using common programming languages. This method provides valuable insights for the development of finite element program creep analysis modules or related scientific research on concrete creep behavior. [ABSTRACT FROM AUTHOR]

Numerical simulation of elastic-wave equation helps us better understand the information of underground structures and elastic-wave imaging has attracted the widespread attention of researchers. Using elastic-wave imaging requires separating the compressional and shear wavefields. Therefore, we develop a novel explicit optimized scheme to simulate the separated elastic wavefield. We construct a kind of 1-norm objective function directly utilizing the dispersion error and employ the simulated annealing algorithm to acquire improved finite-difference operators, whose optimal coefficients can effectively suppress spatial numerical dispersion. Meanwhile, we introduce a rotated staggered-grid (RSG) approach to enhance computational stability. Then, our proposed scheme, called the optimized RSG approach, is applied to the elastic-wave equations and decoupled elastic-wave equations to simulate the decoupled compressional and shear wavefield propagation. Numerical dispersion analysis is consistent with numerical results. The waveform comparison shows that the optimized RSG approach possesses higher accuracy, and several complex models are used to validate the applicability and effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

Due to the low signal-to-noise ratio of raw data, the reliability of data and the accuracy of acoustic source localization are severely affected by fiber optic acoustic sensing technology. To address this issue, this study optimizes the wavelet thresholding method. Firstly, a novel thresholding function is proposed, which achieves denoising while preserving key information through shape adjustment factors. It combines the advantages of both hard and soft threshold functions and has high flexibility and controllability. Secondly, an adaptive threshold calculation method is introduced, utilizing an improved simulated annealing algorithm to optimize threshold selection, reducing the algorithm’s dependence on threshold parameter selection. Through simulation experiments, it has been verified that this research method effectively suppresses noise in the signal and improves data availability. Compared to the original methods, this approach significantly improves the signal-to-noise ratio and demonstrates robustness in simulated tests of real signals.

Abstract 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.

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

Objective To use neural networks and optimization algorithms, establish an obesity level prediction model to assess obesity risk. Methods Perform correlation analysis on 2 111 recorded data collected from participants aged between 14 and 61 years old in Mexico, Peru, and Colombia, and establish a BP neural network obesity level prediction model. At the same time, optimize the number of hidden nodes and transfer function of the model through pruning to find the optimal network structure. In addition, the genetic algorithm and the simulated annealing algorithm were used to optimize the weights and thresholds of the model, ultimately establishing a high-precision and practical GASA-BP neural network obesity level prediction model. Results The R2 of the prediction model was 0.975 1, and the MAE was 0.352, indicating high prediction accuracy and strong practicality. In the process of predicting obesity levels in the model, weight index was the most important, with a correlation of 0.913 with obesity levels. The correlation between overweight members in the family was also relatively strong, with a correlation of 0.505. Conclusion The GASA-BP neural network prediction model performs better than other models in predicting obesity levels, and can make the most accurate prediction of obesity levels, providing guidance and reference for personalized obesity assessments and subsequent prevention and control measures. [ABSTRACT FROM AUTHOR]

This paper explores a multi-objective, multi-period integrated routing and scheduling problem under uncertain conditions for distributing relief to disaster areas. The goals are to minimize costs and maximize satisfaction levels. To achieve this, the proposed mathematical model aims to speed up the delivery of relief supplies to the most affected areas. Additionally, the demands and transportation times are represented using fuzzy numbers to more accurately reflect real-world conditions. The problem was formulated using a fuzzy multi-objective integer programming model. To solve it, a hybrid algorithm combining a multi-objective ant colony system and simulated annealing algorithm was proposed. This algorithm adopts two ant colonies to obtain a set of nondominated solutions (the Pareto set). Numerical analyses have been conducted to determine the optimal parameter values for the proposed algorithm and to evaluate the performance of both the model and the algorithm. Furthermore, the algorithm's performance was compared with that of the multi-objective cat swarm optimization algorithm and multi-objective fitness-dependent optimizer algorithm. The numerical results demonstrate the computational efficiency of the proposed method. [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]

The aim of this study was to improve the diagnostic ability of fall risk classifiers using a Bayesian approach and the Simulated Annealing (SA) algorithm. A total of 47 features from 181 records (40 Center of Pressure (CoP) indices and 7 patient descriptive variables) were analyzed. The wrapper method of feature selection using the SA algorithm was applied to optimize the cost function based on the difference of the mean minus the standard deviation of the Area Under the Curve (AUC) of the fall risk classifiers across multiple dimensions. A stratified 60–20–20% hold-out method was used for train, test, and validation sets, respectively. The results showed that although the highest performance was observed with 31 features (0.815 ± 0.110), lower variability and higher explainability were achieved with only 15 features (0.780 ± 0.055). These findings suggest that the SA algorithm is a valuable tool for feature selection for acceptable fall risk diagnosis. This method offers an alternative or complementary resource in situations where clinical tools are difficult to apply. [ABSTRACT FROM AUTHOR]

Malnutrition has led to growth retardation in many adolescents and health deterioration in adults all over the world. Recently, there has been increasing attention on balanced diets as a preventive measure. This study evaluates the daily diet of a student, aiming to optimize the amino acid score (AAS) across three meals per day. By using balanced diet criteria as constraints, we established a single-objective nonlinear programming model, maximizing AAS as the objective function. The model was solved by using a simulated annealing algorithm, and we obtained a diet that is both balanced and high in AAS. This study helps to raise awareness about individual nutritional needs and provides guidance for dietary structure improvements, thereby contributing to global public health enhancement. [ABSTRACT FROM AUTHOR]

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

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time–frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy. [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 view of the low efficiency of transformer fault diagnosis, an improved grasshopper optimization algorithm is proposed by combining dissolved gas analysis in oil with artificial intelligence method to optimize the transformer fault diagnosis method of Bayesian network. The differential evolution algorithm and simulated annealing algorithm are used to improve the locust algorithm, which improve the optimization ability of the algorithm. The improved locust algorithm is applied to the Bayesian network structure learning to construct the transformer fault diagnosis model, and the method proposed in this study is used to diagnose the transformer fault. The experimental results show that the diagnosis accuracy of this method is 92.7%, which is higher than that of other algorithms [ABSTRACT FROM AUTHOR]

In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection. [ABSTRACT FROM AUTHOR]

As a new swarm intelligence algorithm, sparrow search algorithm (SSA) has the advantages of fewer parameters, simplicity, strong global and local search capability, and has been successfully applied in continuous problem and its engineering applications. Meanwhile, SSA for job shop scheduling problem (JSP) is studied rarely and would arise new problems such as conversion from continuous space to discrete space, falling into local optimum, etc. To address these issues, considering the features of SSA and JSP, the multi-strategy improved sparrow search algorithm (MISSA) is devised to solve minimum makespan of JSP. First, the operation sort based encoding transformation method of SSA for discrete problems is devised. Second, tent chaotic mapping is instead of random generation to initialize sparrow population to expand space of solution. Third, the crossover operation of genetic algorithm is introduced in producers and scroungers positions updating to maintain the population diversity and improve the algorithm search ability. Fourth, the mutation operation of genetic algorithm is adopted in the position update of the vigilance to enhance the local searching capability. Fifth, the simulated annealing algorithm was adopted to avoid the local optimal solution and reach the global best solution. In the end, using 10 classical examples of JSP and one practical scheduling example, comparisons of MISSA with other algorithms are simulated, and the results show that MISSA effectively solves JSP. [ABSTRACT FROM AUTHOR]

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

The Vehicle Routing Problem (VRP) is a well-known discrete optimization problem that has an impact on theoretical and practical applications. In this paper, a freight distribution model that includes a charging system located at the depot, making it feasible for real world-implementation, is proposed. Two different solution methods are proposed and compared: a genetic algorithm (GA) and a population-based simulated annealing (PBSA) with the number of moves increasing during the iterations. Among the variety of algorithm used to solve the VRP, population-based search methods are the most useful, due to the ability to update the memory at each iteration. To demonstrate the practical aspects of the proposed solution a case study is solved using travel time on a real network to evaluate the potentiality for a real-world application.

Abstract Non-renewable energy sources, including fossil fuels, are a type of energy whose consumption rate far exceeds its natural production rate. Therefore, non-renewable resources will be exhausted if alternative energy is not fully developed, leading to an energy crisis in the near future. In this paper, a mathematical model has been proposed for the design of the biomass supply chain of field residues that includes several fields where residue is transferred to hubs after collecting the residue in the hub, the residue is transferred to reactors. In reactors, the residue is converted into gas, which is transferred to condenser and transformers, converted into electricity and sent to demand points through the network. In this paper, the criteria of stability and disturbance were considered, which have been less discussed in related research, and the purpose of the proposed model was to maximize the profit from the sale of energy, including the selling price minus the costs. Genetic algorithm (GA) and simulated annealing (SA) algorithm have been used to solve the model. Then, to prove the complexity of the problem, different and random examples have been presented in different dimensions of the problem. Also, the efficiency of the algorithm in small and large dimensions was proved by comparing GA and SA due to the low deviation of the solutions and the methods used have provided acceptable results suitable for all decision-makers. Also, the effectiveness of the algorithm in small and large dimensions is proven by comparing the genetic algorithm and simulated annealing, and the genetic algorithm's values are better, considering the deviation of 2.9%.and have provided solution methods suitable for all decision makers.

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

Nowadays, the Internet of Things (IoT) networks provide benefits to humans in numerous domains by empowering the projects of smart cities, healthcare, industrial enhancement and so forth. The IoT networks include nodes, which deliver the data to the destination. However, the network nodes' connectivity is affected by the nodes' removal caused due to the malicious attacks. The ideal plan is to construct a topology that maintains nodes' connectivity after the attacks and subsequently increases the network robustness. Therefore, for constructing a robust scale-free network, two different mechanisms are adopted in this paper. First, a Multi-Population Genetic Algorithm (MPGA) is used to deal with premature convergence in GA. Then, an entropy based mechanism is used, which replaces the worst solution of high entropy population with the best solution of low entropy population to improve the network robustness. Second, two types of Edge Swap Mechanisms (ESMs) are proposed. The Efficiency based Edge Swap Mechanism (EESM) selects the pair of edges with high efficiency. While the second ESM named as EESM-Assortativity, transforms the network topology into an onion-like structure to achieve maximum connectivity between similar degree network nodes. Further, Hill Climbing (HC) and Simulated Annealing (SA) methods are used for optimizing the network robustness. The simulation results show that the proposed MPGA Entropy has 9% better network robustness as compared to MPGA. Moreover, both the proposed ESMs effectively increase the network robustness with an average of 15% better robustness as compared to HC and SA. Furthermore, they increase the graph density as well as network's connectivity. [ABSTRACT FROM AUTHOR]

In the process of grid connection of irregular output micro‐grid systems, it is of great research significance to make micro‐grid scheduling operate more effectively and economically. To minimize the environmental and total operating costs of the micro‐grid intelligent scheduling system during grid connection, this study proposes a micro‐grid intelligent scheduling model based on an optimized particle swarm optimization algorithm. The optimized particle swarm algorithm mainly improves the problem of the particle swarm algorithm being prone to local optima, making it reduce the possibility of local convergence. Comparative tests were conducted on the improved IPSO algorithm, and the results showed that the non‐dominated solution concentration pollution emissions obtained by the improved IPSO algorithm were at least 18326 Ib, which was lower than the comparison algorithm. In the empirical analysis of the intelligent scheduling model based on the improved IPSO algorithm, the convergence and satisfaction of the model were 0.0054 and 0.922, respectively, which were superior to the comparison algorithm. The above results indicate that the intelligent scheduling model for micro‐grid based on the improved IPSO algorithm has good economy and stability. Compared with traditional energy dispatch methods, this energy dispatch mode has higher economic benefits and is of great significance for promoting the development of micro‐grid. [ABSTRACT FROM AUTHOR]

Maintaining the terminal voltage of a power system is a crucial process and this can be achieved via a system named automatic voltage regulator (AVR). However, an AVR needs an appropriate control method. In this context, this article proposes a novel Harris hawks optimization (HHO) and simulated annealing (SA) technique which can be used for AVR. The proposed optimization technique (HHO‐SA) combines the good exploration feature of HHO with the exceptional local search feature of SA. The HHO‐SA algorithm is introduced as a novel design method to obtain the optimum parameters for proportional + integral + derivative plus second order derivative (PID + DD) controller adopted in the AVR. Time domain objective function of the system is effectively minimized and the best PID + DD parameters are obtained. The analysis of statistical tests, convergence, transient and frequency responses, root locus, and disturbance rejection along with robustness are conducted for verifying the efficiency of the HHO‐SA algorithm. Also, the performance of the HHO‐SA tuned PID + DD controller on AVR is compared with the original HHO tuned PID + DD along with PID, FOPID, and PID + DD controllers that are adjusted by state‐of‐the‐art metaheuristic methods. The practical implementation of the proposed controller is also demonstrated in this work. The extensive simulation results and comparisons with the existing controllers adopting the same set of data demonstrate the superior control performance and good robustness of the HHO‐SA tuned PID + DD controller. [ABSTRACT FROM AUTHOR]

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Avionics systems are a crucial part of aircraft, and the heterogeneity of resources also leads to load differentials within these systems. Inefficient load balancing technology faces the dual challenge of over-utilization and under-utilization of resources, which results in the decline of service performance (in the case of over-utilization) or the waste of resources (in the case of under-utilization). However, it is necessary to control avionics systems via an efficient load balancing method under time constraints and resource states. Therefore, this paper proposes a load balancing method for avionics systems using both artificial bee colony and simulated annealing algorithms. First, the load balancing model of avionics systems is established; this model can reflect the demands of the tasks for the resources in detail. Then, the load balancing of avionics systems is realized by artificial bee colony and simulated annealing algorithms, the hybrid algorithm not only retains the advantages of simple and easy implementation of ABC algorithm, but also utilizes the probability jump of SA algorithm to jump out of the local extreme and achieve the effect of global optimization. Finally, compared with the existing algorithms, the experimental results show that the algorithm proposed in this paper produces a good and stable load balance performance. [ABSTRACT FROM AUTHOR]