Traditional Simulated Annealing Algorithm Research Articles

Timing-Driven Simulated Annealing for FPGA Placement in Neural Network Realization

The simulated annealing algorithm is an extensively utilized heuristic method for heterogeneous FPGA placement. As the application of neural network models on FPGAs proliferates, new challenges emerge for the traditional simulated annealing algorithm in terms of timing. These challenges stem from large circuit sizes and high heterogeneity in the block proportions typical in neural networks. To address these challenges, this study introduces a timing-driven simulated annealing placement algorithm. This algorithm integrates cluster criticality identification during the cluster selection phase, which enhances the probability of high-criticality cluster selection. In the cluster movement phase, the proposed method employs an improved weighted center movement for high-criticality clusters and a random movement strategy for other clusters. Experimental evidence demonstrates that the proposed placement algorithm decreases the average wire length by 1.52% and the average critical path delay by 5.03%. This improvement in performance is achieved with a marginal increase of 5.01% in runtime, as compared to VTR8.0.

Analysis of the application of path finding system based on efficiency improvement in smart tourism

With the proposal and development of intelligent tourism destination, the problem of tourism route planning has become a hot topic in tourism research. This study proposes an intelligent tourism route planning model based on efficiency improvement for Guangxi tourism route planning. In order to improve the efficiency of the model, this paper combines ant colony algorithm with genetic algorithm and introduces smoothing factor and guiding factor, and obtains genetic algorithm (genetic algorithm-path smoothing ant colony optimization) with changing probability. Experimental results show that the convergence speed of the algorithm is fast, and it can converge in 78 iterations, and the operation efficiency is higher. The result error of the proposed algorithm is 1.39, which is smaller than that of other comparative algorithms. In the actual application effect evaluation, the average satisfaction of the planned route of the intelligent tourism route planning model based on efficiency improvement is 15.38, which is higher than that of the traditional genetic algorithm and simulated annealing algorithm. It shows that the model can not only efficiently plan the best travel route, but also comprehensively consider the whole trip of tourists, so that tourists can have a better travel experience. The research provides an intelligent route planning method that is more efficient and more in line with tourists' needs for tourism development.

Exploring the design of ecological and energy-saving residential buildings adapted to climate change based on a simulated annealing algorithm

Abstract In the context of the digital development of big data and information in recent years, performance-based residential building design and its optimization methods have gradually become a research hotspot of domestic and international attention. The simulated annealing algorithm is a major component of residential buildings’ optimal energy efficiency design. Although the crossover operator under the computational traditional simulated annealing algorithm incorporates the respective advantages of the genetic algorithm and simulated annealing algorithm, the purely optimal genetic simulated annealing algorithm is more effective for the evaluation of index analysis and better reflects the superiority of the algorithm. In the Energy Plus model, the three algorithms were used to analyze the design indexes of ecological energy-efficient residential buildings, and the SS values of the MOSA algorithm fluctuated between 10% and 24%, and the performance of the recommended genetic simulated annealing algorithm was significantly better than the other two algorithms. This study effectively solves the difficulties of low efficiency and high failure rate common in design practice when using this technique and is of historical importance to the development of energy-efficient residential buildings in China.

Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm

Logistics distribution is a collection of interrelated organizations and facilities. There is a waste of cost and time in many links. Therefore, it is particularly important to use information technology to improve distribution efficiency. Under the constraints of delivery vehicle cost and time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines the global search ability of the genetic algorithm (GA) and the simulated annealing algorithm (SA) with strong local search ability to solve the vehicle routing problem with time windows (VRPTW). The perturbation factor is introduced to improve the local search, and the crossover method is optimized to obtain more efficient genetic operators by using population information. In this paper, combined with the actual application case, the simulation experiment is carried out in MATLAB. The experimental results show that, compared with the traditional genetic algorithm and simulated annealing algorithm, the total cost of the improved genetic simulated annealing algorithm is reduced by about 15%, which provides a more suitable vehicle route planning scheme.

A Modified Simulated Annealing (MSA) Algorithm to Solve the Supplier Selection and Order Quantity Allocation Problem with Non-Linear Freight Rates

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time.

Research on the optimization of energy consumption for multi-priority tasks in mobile computing offloading

To solve the problem of insufficient energy consumption for multi-priority tasks in the computing and unloading environments of mobile devices, we designed evaluation methods to improve the traditional simulated annealing algorithm and obtain the optimal allocation scheme for these tasks under multiple computing resources. Firstly, we proposed the task model and discussed the classification and definition of task priority as well as the procedure of task processing in detail. Secondly, a calculation model for the energy consumption of mobile devices is provided. Thirdly, an evaluation mechanism is designed to evaluate the unloading distribution scheme of mobile devices effectively. Finally, the improved traditional simulated annealing algorithm was applied to find the optimal distribution scheme for this computing environment. All allocation schemes obtained in this study were compared and analyzed via simulation. The results showed that the proposed algorithm can reduce the energy consumption of mobile devices more significantly, shorten the system response time, and complete high-priority tasks based on time constraints.

Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.

A Two-Step Simulated Annealing Algorithm for Spectral Data Feature Extraction

To address the shortcomings in many traditional spectral feature extraction algorithms in practical application of low modeling accuracy and poor stability, this paper introduces the "Boruta algorithm-based local optimization process" based on the traditional simulated annealing algorithm and proposes the "two-step simulated annealing algorithm (TSSA)". This algorithm combines global optimization and local optimization. The Boruta algorithm ensures that the feature extraction results are all strongly correlated with the dependent variable, reducing data redundancy. The accuracy and stability of the algorithm model are significantly improved. The experimental results show that compared with the traditional feature extraction method, the accuracy indexes of the inversion model established by using the TSSA algorithm for feature extraction were significantly improved, with the determination coefficient R2 of 0.9654, the root mean square error (RMSE) of 3.6723 μg/L, and the mean absolute error (MAE) of 3.1461 μg/L.

An Improved Simulated Annealing-Based Decision Model for the Hybrid Flow Shop Scheduling of Aviation Ordnance Handling.

Aviation ordnance handling is critical to the firepower projection of the time-critical cyclic flight operation on aircraft carriers. The complexity of the problem depends on the supply and demand features of ordnance. This paper examines the scheduling of aviation ordnance handling of an operational aircraft carrier under the framework of hybrid flow shop scheduling (HFS) and derives a method based on the simulated annealing (SA) algorithm to get the HFS problem's solution. The proposed method achieves the minimum possible flow time by optimizing the ordnance assignment through different stages. The traditional SA algorithm depends heavily on the heuristic scheme and consumes too much time to compute the optimal solution. To solve the problem, this paper improves the SA by embedding a task-based encoding method and a matrix perturbation method. The improved SA remains independent of the heuristic scheme and effectively propagates the local search process. Since the performance of SA is also influenced by its embedded parameters, orthogonal tests were carried out to carefully compare and select these parameters. Finally, different ordnance loading plans were simulated to reveal the advantage of the improved SA. The simulation results show that the improved SA (ISA) can generate better and faster solution than the traditional SA. This research provides a practical solution to stochastic HFS problems.

Analysis of the material distribution system of wise information technology of 120 under deep learning

In order to explore the feasibility of using deep learning in the Wise Information Technology of 120 (WIT120) material distribution system under deep learning, in the study, first, the Hopfield neural network and simulated annealing (SA) algorithm are used to study the path optimization in the WIT120 material distribution system. Aiming at the poor optimization and robustness of the traditional Hopfield neural network and the low optimization efficiency of SA algorithm, in this study, a memory device is added to the SA algorithm, the distribution starting points of the two algorithms are fixed, and an improved hybrid algorithm is designed by combining Hopfield neural network and SA algorithm. Then, the improved hybrid algorithm of the traditional Hopfield neural network and SA algorithm is used in the actual medical material distribution system, and comparative analysis of the distribution route, cost, and scheduling time before and after using the proposed algorithm is performed. Besides, the difference between the proposed algorithm and other algorithms in path optimization and path planning efficiency is compared and analyzed. The results show that the improved hybrid algorithm proposed in this research reduces the total mileage of distribution by 46.9%, costs by 33.9%, and the time required for scheduling by 99% after applied in the medical material distribution system. It is also better than traditional analog algorithms and neural networks in distribution path optimization. The path optimization results show that the shortest path produced by the improved hybrid algorithm in this study is 27.85, which is superior to the traditional SA algorithm, Hopfield neural network, and genetic algorithm (the shortest paths obtained are 41.21, 44.63 and 36.48, respectively). Compared with other optimization algorithms, the path planning efficiency of the hybrid algorithm is the highest (the amount of distribution tasks completed in unit time is 22). In conclusion, the improved hybrid algorithm proposed in this study can effectively reduce distribution costs, shorten transportation routes, and improve optimization efficiency, indicating that the hybrid algorithm proposed in this study is effective and practical when applied in the medical material distribution system, which can provide reference value for medical material distribution and provide data support for the management of medical material distribution.

The Mathematical Model and an Genetic Algorithm for the Two-Echelon Electric Vehicle Routing Problem

In order to cope with the challenges of high cargo load and high timeliness distribution in logistics industry, as well as to alleviate the current situation of oil resource depletion and air pollution, this study established a mathematical model of two-echelon electric vehicle routing problem (2E-EVRP) and design a heuristic algorithm. The 2E-EVRP can be divided into the multiple depot vehicle routing problem (MDEVRP) and the split delivery vehicle routing problem (SDVRP). The proposed genetic algorithm is used to solve the MDEVRP, and the actual case of a logistics company in Beijing is taken as the calculation experiment, so as to verify the feasibility of the proposed algorithm and provide decision-making reference for the development of logistics enterprises. The results show that the total path length obtained by the proposed algorithm is optimized by 20.82 kilometers compared with the traditional simulated annealing algorithm.

Distribution reconstruction of non-uniform combustion field based on improved simulated annealing algorithm

In this paper, a method of selecting the optimal transition lines’ combination is analyzed to measure the absorption spectrum of the non-uniform combustion flow field, which is used to solve the basic two-region distribution, and an improved simulated annealing algorithm (ISA) is proposed for reconstructing the field distribution of the combustion flow field, in order to solve the problems of slow convergence speed and low efficiency of the traditional simulated annealing algorithm. By modifying the model perturbation mode and annealing strategy, the efficiency of the algorithm and the chance to jump out of the local optimal space are further improved. According to the numerical simulation results, more transitions are helpful in improving the accuracy of combustion field reconstruction and making the reconstruction less sensitive to noise. It is worth noting that the optimal transitions’ combination is better than the non-optimal transitions’ combination with more transitions included. In this paper, three different combustion models are constructed to verify the effectiveness of the improved algorithm. A comparison between the reconstruction results of the traditional simulated annealing algorithm and the improved simulated annealing algorithm shows that both algorithms have the same precision but the latter algorithm has a higher operating efficiency, and a faster running time (nearly 40 times faster than the former algorithm). At the same time, the simulation results also show that the reconstruction accuracy will decrease slightly with the complication of combustion flow field. By building the TDLAS-HT measurement system in the laboratory and using 8 × 8 orthogonal optical path arrangement, the two different combustion states formed before and after placing the steel rod in the flat flame furnace are reconstructed, the results show that the reconstruction distribution is basically consistent with the original distribution, and the reconstructed distribution well shows the combustion characteristics of the original distribution of the flame field. The effectiveness of the proposed method is verified by numerical simulation and verification tests. Under the condition of the same reconstruction accuracy as the reconstruction accuracy of the traditional simulated annealing algorithm, the higher operating efficiency is helpful in reconstructing the rapidly changing turbulent field, which has some guiding significance for the hyperspectral reconstruction of temperature and concentration distribution in the combustion flow field.

A Hybrid Chemical Reaction Optimization Algorithm for Solving 3D Packing Problem

This paper combines the chemical reaction optimisation algorithm with the greedy algorithm to solve the three-dimensional bin packing problem (3D-BPP). Traditionally, the 3D-BPP is solved with algorithms based on particle swarm optimisation, genetic algorithm, or simulated annealing algorithm, etc. The chemical reaction optimisation algorithm is proposed in recent years, which has the advantages of traditional simulated annealing algorithm and has a faster convergence speed. After making appropriate adjustments to the chemical reaction optimisation algorithm, the experiment was performed on classic 320 examples. The results show that the proposed algorithm outperforms the existing algorithms such as greedy variable neighbourhood descent (GVND) and biased random key genetic algorithm (BRKGA).

Aerodynamic optimization of a SCO2 radial-inflow turbine based on an improved simulated annealing algorithm

Supercritical carbon dioxide plays a vital role in the development of power generation applications. It owns the characteristics of high density and low viscosity, which can ensure a compact structure for turbomachinery. With the blossom of optimization algorithm, an interdisciplinary research which applies optimization method to a traditional design process of turbomachinery can accelerate the course and promote the validity by leaps and bounds. We improve the traditional simulated annealing algorithm and establish an optimization process containing the optimization of rotor meridian plane and nozzle profile. This process can effectively reduce the computation time by establishing a surrogate model of coarse mesh simulation. The effects of traditional simulated annealing algorithm (SAA), genetic algorithm (GA) and improve simulated annealing algorithm (ISAA) are compared. As a result, we realize a maximum of 4.94% promotion for isentropic efficiency in ISAA computation. Also, ISAA method saves the computation time by 59.6% compared to GA and by 41.5% compared to SAA. Applying ISAA optimization method to the turbine in a kW-scale solar-driven Brayton cycle power system, we realize a 1.17% increase for the system efficiency.

Localization of Multiple RF Sources Based on Bayesian Compressive Sensing Using a Limited Number of UAVs With Airborne RSS Sensor

Locating multiple Radio Frequency (RF) sources by Unmanned Aerial Vehicles (UAVs) using Received Signal Strength (RSS) measurements attracts extensive attention for its intrinsic simplicity in hardware and low cost. However, the signals from multiple RF sources are aggregated at the airborne sensors, making it hard to achieve multi-emitter localization with a limited number of UAVs. In this paper, the Bayesian compressive sensing (BCS) approach is applied to estimating ground targets’ locations exploiting the RSS data collected by the onboard sensors. Meanwhile, to support the BCS approach, which requires sufficient measurements to achieve multi-emitter localization, a sensing architecture comprising a limited number of UAVs is presented. Besides, to optimize the localization result, we put forward a trajectory planning-based Bayesian compressive sensing (TPBCS) algorithm. This algorithm dynamically plans UAVs’ trajectories based on the adaptive simulated annealing (ASA) algorithm, which can bring down the computational cost in UAV path planning compared to the traditional simulated annealing (SA) algorithm. The effectiveness and robustness of the approach are validated by simulations in the end.

A patient-specific modelling method of blood circulatory system for the numerical simulation of enhanced external counterpulsation

Lumped parameter model (LPM) is a common numerical model for hemodynamic simulation of human’s blood circulatory system. The numerical simulation of enhanced external counterpulsation (EECP) is a typical biomechanical simulation process based on the LPM of blood circulatory system. In order to simulate patient-specific hemodynamic effects of EECP and develop best treatment strategy for each individual, this study developed an optimization algorithm to individualize LPM elements. Physiological data from 30 volunteers including approximate aortic pressure, cardiac output, ankle pressure and carotid artery flow were clinically collected as optimization objectives. A closed-loop LPM was established for the simulation of blood circulatory system. Aiming at clinical data, a sensitivity analysis for each element was conducted to identify the significant ones. We improved the traditional simulated annealing algorithm to iteratively optimize the sensitive elements. To verify the accuracy of the patient-specific model, 30 samples of simulated data were compared with clinical measurements. In addition, an EECP experiment was conducted on a volunteer to verify the applicability of the optimized model for the simulation of EECP. For these 30 samples, the optimization results show a slight difference between clinical data and simulated data. The average relative root mean square error is lower than 5%. For the subject of EECP experiment, the relative error of hemodynamic responses during EECP is lower than 10%. This slight error demonstrated a good state of optimization. The optimized modeling algorithm can effectively individualize the LPM for blood circulatory system, which is significant to the numerical simulation of patient-specific hemodynamics.

Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle

This paper studies the capacity of electric vehicle charging station (EVCS) and energy storage, and the optimization problem and model of electric vehicle (EV) charging scheduling plan. Based on the alternative energy storage effect of EVs, it is committed to improve the renewable energy consumption capacity in micro-grid, reduce the EVCS and energy storage capacity, and improve the comprehensive benefits of micro-grid investors. In this paper, considering the factors that affect the efficiency and system security, the determination model of EVCS and energy storage capacity is established, and the traditional simulated annealing algorithm (SA) is improved to create a disturbance mechanism for the optimization of charging scheduling plan, and the joint optimization is realized. The empirical analysis results show that the EVCS and energy storage capacity are reduced by about 50%, and the benefit of micro-grid investors is increased by about 7%. Therefore, this study can provide solutions for the planning of micro-grid with distributed energy, conventional users, energy storage and adjustable load, maximize renewable energy consumption and improve the satisfaction of all participants. Keywords: Electric vehicle charging station; Alternative energy storage effect; Net annual value; Improved simulated annealing algorithm; Joint capacity optimization.

Multi-agent list-based noising algorithm for protein structure prediction

Protein structure prediction (PSP) with ab initio model problem is still a challenge in bioinformatics on account of high computational complexity. To solve this problem within a limited time and resource, a multi-agent list-based nosing (MLBN) algorithm is presented. MLBN contains three main features. First, a flexible noising list is designed to adjust the solution acceptance condition according to the convergence. An adaptive multiple sampling strategy is included to provide a strong exploitation. A parallel framework explores the searching space in a more effective way. Compared to traditional Simulated Annealing (SA) algorithm, MLBN introduces only one extra parameter for the length of noising list and it is insensitive to specific problems. Conducted experiments in a range of protein sequences indicate MLBN performs better than, or at least is comparable with, several state-of-the-art algorithms for PSP.

Multi-agent list-based noising algorithm for protein structure prediction

Protein structure prediction (PSP) with ab initio model problem is still a challenge in bioinformatics on account of high computational complexity. To solve this problem within a limited time and resource, a multi-agent list-based nosing (MLBN) algorithm is presented. MLBN contains three main features. First, a flexible noising list is designed to adjust the solution acceptance condition according to the convergence. An adaptive multiple sampling strategy is included to provide a strong exploitation. A parallel framework explores the searching space in a more effective way. Compared to traditional Simulated Annealing (SA) algorithm, MLBN introduces only one extra parameter for the length of noising list and it is insensitive to specific problems. Conducted experiments in a range of protein sequences indicate MLBN performs better than, or at least is comparable with, several state-of-the-art algorithms for PSP.

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream. To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional (3-D) space, is a strategic problem due to its large-scale, complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.