Genetic Annealing Algorithm Research Articles

Health Monitoring of Civil Engineering Structures Using Simulated Annealing Genetic Algorithm

Health Monitoring of Civil Engineering Structures Using Simulated Annealing Genetic Algorithm

Mission Planning Method for Dense Area Target Observation Based on Clustering Agile Satellites

To address the mission planning challenge for agile satellites in dense point target observation, a clustering strategy based on an ant colony algorithm and a heuristic simulated genetic annealing optimization algorithm are proposed. First, the imaging observation process of agile satellites is analyzed, and an improved ant colony algorithm is employed to optimize the clustering of observation tasks, enabling the satellites to complete more observation tasks efficiently with a more stable attitude. Second, to solve for the optimal group target observation sequence and achieve higher total observation benefits, a task planning model based on multi-target observation benefits and attitude maneuver energy consumption is established, considering the visible time windows of targets and the time constraints between adjacent targets. To overcome the drawbacks of traditional simulated annealing and genetic algorithms, which are prone to local optimal solution and a slow convergence speed, a novel Simulated Genetic Annealing Algorithm is designed while optimizing the sum of target observation weights and yaw angles while also accounting for factors such as target visibility windows and satellite attitude transition times between targets. Ultimately, the feasibility and efficiency of the proposed algorithm are substantiated by comparing its performance against traditional heuristic optimization algorithms using a dataset comprising large-scale dense ground targets.

Broadband 3-bit coding metasurface antenna with integrated radiation and scattering performance

This paper presents the design of an integrated metasurface antenna, which combines a central radiating patch with a quadru-arc (QAS) structure. The metasurface antenna simultaneously achieves high-gain radiation and complex scattering functionality. The modulation of the radiation function is primarily achieved through phase manipulation of the power division feed network, while modulation of the X-polarization scattering function is mainly accomplished by adjusting the arc of the QAS. The effectiveness of this design is verified by designing two metasurface antennas with distinct functionalities. The feed network phases are arranged in a checkerboard pattern in the first approach, resulting in four-beam radiation with a gain of 16 dBi per beam. Additionally, the scattering component utilizes eight scattering structures with a phase difference of 45 degrees to form a 3-bit coding, enabling vortex beam scattering. The second configuration arranges the feed network in phase with the deflected beam, resulting in a deflected beam radiation pattern characterized by a gain of 22.3 dBi. The scattering function is optimized using a simulated annealing-genetic algorithm for phase alignment, resulting in the achievement of RCS reduction across a wide bandwidth range of 8-24 GHz. The proposed metasurface antenna is ultimately fabricated and subjected to rigorous measurements.

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data.

Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved model-building efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

The Collapse Deformation Control of Granite Residual Soil in Tunnel Surrounding Rock: A Case Study

Since it is difficult to control tunnel construction in weak and broken surrounding rock, the large deformation of tunnel surrounding rock has always been a great danger to construction safety. In this study, the characteristics of a tunnel collapse deformation and its causal factors are analysed as an example of a large deformation accident in a tunnel with granite residual soil in the surrounding rock. First, a support vector regression algorithm optimized by the genetic annealing algorithm (GASA-SVR) is used to construct a back-analysis model of the surrounding rock mechanical parameters. Then, based on the field measured data, the surrounding rock mechanical parameters of the tunnel are inverted. Finally, the finite element method is used to simulate different surrounding rock deformation control schemes, and the optimal deformation control measures are obtained. The results show that the simulated annealing algorithm (SA) enhances the local search capability of the genetic algorithm (GA) so that the hyperparameter optimization solution of the support vector regression (SVR) is the global optimal solution. The parameters obtained from the inversion of the GASA-SVR model are simulated in PLAXIS3D within 4.0% of the measured values in the field. With the application of optimized support measures at the site, the deformation of the surrounding rock has been effectively controlled.

Multi-Dimensional Moving Target Defense Method Based on Adaptive Simulated Annealing Genetic Algorithm

Due to the fine-grained splitting of microservices and frequent communication between microservices, the exposed attack surface of microservices has exploded, facilitating the lateral movement of attackers between microservices. To solve this problem, a multi-dimensional moving target defense method based on an adaptive simulated annealing genetic algorithm (MD2RS) is proposed. Firstly, according to the characteristics of microservices in the cloud, a microservice attack graph is proposed to quantify the attack scenario of microservices in the cloud so as to conveniently and intuitively observe the vulnerability of microservices in the cloud and the dependency relationship between microservices. Secondly, the security gain and resource cost are quantified for the key nodes selected by measuring the degree of dependence of each node according to the degree centrality. Finally, the Adaptive Simulated Annealing Genetic Algorithm (ASAGA) is used to solve the optimal security configuration information of the moving target defense, that is, the combination of the number of copies of the multi-copy deployment and the rotation cycle of the dynamic rotation of microservices, in order to quickly evaluate the security risks of microservices and optimize the security policy. Experiments show that the defense return rate of MD2RS is 85.95% higher than that of the mainstream methods, and the experimental results are conducive to applying this method to the dynamic defense of microservices in the cloud.

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

Enhancing supply chain management in the physical internet: a hybrid SAGA approach

This paper introduces an advanced inventory replenishment optimization approach tailored for the Physical Internet (PI), addressing the dynamic and complex nature of this environment. We propose a hybrid Simulated Annealing–Genetic Algorithm (SA–GA), engineered to optimize the balance between exploration and exploitation, ensuring adaptability and efficiency in a variety of PI contexts. The study also presents an enriched mathematical model integrating dynamic demand, and multi-objective optimization. The SA–GA algorithm emerges as a novel contribution, characterized by its computational efficiency and adaptability, marking an advancement in PI inventory management. The incorporation of real-time data analytics in our dynamic inventory replenishment strategy enhances adaptability and responsiveness, while the robust mathematical model offers a versatile tool for both theoretical analysis and practical application. Collectively, these innovations help bridge existing gaps in PI inventory management and serve as a reference for other similar studies.

Multi-Objective Design of UAS Air Route Network Based on a Hierarchical Location–Allocation Model

This research concentrates on the Unmanned Aircraft System (UAS) demand sites’ hierarchical location–allocation problem in air route network design. With demand sites (locations where UAS operations are requested) organized and allocated according to the spatial hierarchy of UAS traffic flows, the hierarchical structure guarantees resource conservation and economies of scale through traffic consolidation. Therefore, in this paper, the UAS route network with a three-level hierarchy is developed under a multi-objective decision-making framework, where concerns about UAS transportation efficiency from the user side and construction efficiency from the supplier side are both simultaneously considered. Specifically, a bi-level Hybrid Simulated Annealing Genetic Algorithm (HSAGA) with global and local search combined is proposed to determine the optimal number, location, and allocation of hierarchical sites. Moreover, using the information of site closeness and UAS demand distribution, two problem-specific local search operators are designed to explore elite neighborhood regions instead of all the search space. A case study based on the simulated UAS travel demand data of the Beijing area in China was conducted to demonstrate the effectiveness of the proposed method, and the impact of critical parameter settings on the network layout was explored as well. Findings from this study will offer new insights for UAS traffic management in the future.

Customized passenger path optimization for airport connections under carbon emissions restrictions

In response to the challenge of optimizing customized passenger transport paths for airport connections while taking carbon emissions constraints into account, this paper proposes an optimization model that minimizes the total cost by addressing passenger time window constraints, determining optimal passenger transport paths, and optimizing factors like the number of drop-off stations and vehicle occupancy rates. The total cost comprises the operational expenses of customized passenger transport businesses and travel time costs per passenger. We develop an annealing genetic algorithm to solve the model and provide a case analysis. Our findings indicate that the algorithm and the model empower decision-makers to swiftly select passenger transport path schemes that minimize the total cost with their specific requirements.

Research on rural power grid planning method based on improved genetic annealing algorithm

Current power grid planning mainly relies on planning experience in personnel selection schemes. Planning results of subjectivity are stronger, making the planning after the network loss difficult to meet the requirements. According to the above defects, research on rural power grid planning methods based on an improved genetic annealing algorithm is proposed. Using the directed graph, the operation mode of the rural power grid is analyzed, and the network load of different distributed power sources is calculated. The multi-objective programming model is established from two aspects of the economy and environmental protection of the rural power grid. The annealing algorithm is introduced in the crossover and mutation stage of the genetic algorithm. In the example experiment, the cost reduction of the improved genetic annealing algorithm is 57.45%, and the network loss rate is lower than that of the other planning methods, which makes the network power supply more reliable.

EV Charging Path Distribution Solution Based on Intelligent Network Connection

The long queuing time for electric vehicles to charge under intelligent network connection leads to low distribution efficiency. Therefore, this paper proposes a strategy to predict the probability of queues forming for electric vehicles arriving at charging stations under intelligent network connection. Both the dynamic demand of customers and the characteristics of the alternating influence of charging vehicles should be considered when studying such problems. Based on the above problem characteristics, a real-time dynamic charging selection strategy is developed by predicting the probability of other vehicles in the region going to the charging station. A distribution path optimization model based on intelligent network connection and queuing theory is proposed for electric logistics vehicles in charging mode, taking into account the time window constraint and the influence of charging vehicles when using intelligent network connection for path planning. The objective is to minimize the total cost, and the route for electric logistics vehicles is adjusted in real time. This is solved by an improved hybrid genetic-annealing algorithm. The experimental results show that this paper obtains real-time dynamic road information and charging information with the help of intelligent network connection. It predicts the queuing probability of electric vehicles by combining with queuing theory, which can help select a more suitable charging location and timing for electric logistics vehicles. This can effectively avoid peak periods and reduce waiting times. By comparing with other models, this paper’s model can save the distribution cost of electric vehicles.

Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles

Article Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles Sihao Chen 1,2, Zhenfeng Wang 1,2, Zhengbai Liu 3, Xianyi Yang 1,2, and Heng Wang 1,2,* 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 College of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: dawn.wangh@henau.edu.cn Received: 8 May 2023 Accepted: 29 May 2023 Published: 21 June 2023 Abstract: When an intelligent connected vehicle (ICV) autonomously completes an intelligent driving assignment, the decision planning layer needs to plan an optimal path from the starting location to the target location for the vehicle, which is referred to as global path planning (GPP) for the ICV. For the GPP of ICVs undertaking long-distance and multi-location driving assignments, a fixed open travelling salesman problem (TSP) was constructed in conjunction with travel time analysis. To better address this issue, a genetic annealing algorithm (GAA) was proposed, and corresponding simulations were conducted using genetic algorithm, ant colony algorithm, and GAA respectively. Based on the optimization processes and results, the GAA outperformed the traditional genetic algorithm and ant colony algorithm in tackling this issue. Therefore, the method proposed in this paper can be applied to the global path planning platform for intelligent networked vehicles.

Lithium battery sorting method for high-rate operating conditions

Lithium batteries are increasingly used in electric vehicle applications. However, different manufacturing processes and technical constraints lead to battery inconsistency, even for batteries in the same production batch. High-rate discharging negatively affects battery consistency and results in service life reduction. A multi-parameter sorting method at high-rate operation was proposed in this study. The method was applied to sort batteries for cars. The sorted datasets were compared and analyzed by the fuzzy C-mean clustering method, the K-means clustering method, and the simulated annealing genetic algorithm. The comparisons proved that the genetic annealing algorithm was more suitable for battery classification. The clustered batteries were assembled into modules in series and parallel for experimental validation. The test results showed that the battery module cycle life was improved.

Modelling and comparing two modes of sharing parking spots at residential area: Real‐time and fixed‐time allocation

AbstractThis research aims to improve the utilization efficiency of parking facilities in residential areas. The real‐time and fixed‐time shared parking spot allocation models based on a time window constraint are proposed, respectively. The real‐time model adopts the dynamic response service mechanism, introducing a multi‐objective decision weighting method to construct the weighted evaluation function. Then, the 0–1 planning model with user optimization is established, utilizing branch‐bound algorithm for a solution. The fixed‐time model adopts the periodic service mechanism, where a rejection penalty factor is introduced to add penalty cost. Then, the 0–1 programming model with system optimization is constructed, where genetic annealing algorithm solves the large‐scale calculation problem. The results from this case study illustrate the fixed‐time allocation mode has more balanced utilization of parking facilities, whereas over‐utilization of preferred parking lots occurs in the real‐time model; additionally, when supply and demand are in balance, the fixed‐time model can obtain higher system revenues, reduce effective rejection rate by 9.43%, and increase resource utilization efficiency by 5.28%. In conclusion, the real‐time allocation mode reflects the advantage of a user's optimal allocation mode when supply is greater than demand; conversely, the fixed‐time allocation mode has the advantage of optimum system resources utilization efficiency.

Comparison of SAGA and GA in Wind Turbine Layout Optimization Based on Modified Two-dimensional Wake Model or Jensen Model

In this paper, Simulated Annealing Genetic Algorithm (SAGA) and a new two-dimensional wake model called 2D_k Jensen model are adopted for optimal wind turbine layout (WTL) in the wind farms and are compared with Genetic Algorithm (GA) and Jensen model, respectively, aiming to minimize the investment cost and maximize the wind power generation as much as possible. The influence of the radial distribution of wake on the equivalent wind speed in the wake superposition region is considered. In the case of single wind direction and single speed, total output power and energy extraction efficiency are both improved when SAGA is applied to the two model conditions respectively, especially for the WTL using the 2D_k Jensen model, these two aspects are significantly improved by 13.75% and 24.10%, respectively, and the objective function is reduced by 19.05%. The results demonstrate that SAGA is more conducive to solving the practical configuration optimization of wind turbines, compared with the original GA.

Multi-User Dynamic Computation Offloading and Resource Allocation in 5G MEC Heterogeneous Networks with Static and Dynamic Subchannels

With the rapid development of Mobile Edge Computing (MEC) technology, the computationally intensive requests of end devices can be offloaded to MEC servers directly, which equipped at the edge of wireless networks. Through offloading, the performances such as the execution delay as well as the energy consumption can be effectively improved, which can significantly enhance the quality of user experience. Given the dynamics and randomness of computation requests arrival, the energy in the battery, the radio network environment, and the computation resource in the MEC server, it is a challenge to perform efficient offloading. Based on these problems, this paper proposes dynamic optimization schemes with queuing theory for the cases of the static subchannel and dynamic subchannel during a time slot separately in 5G MEC heterogeneous networks with multiple MDs equipped with the function of energy harvesting. In the schemes, offloading decisions and radio allocation strategies will be dynamically coordinated. They are also jointly allocated along with changing wireless communication resources and computation demands aiming to minimize the system average execution delay. Specifically, it is assumed that the offloading requests can be transmitted through either macro base stations or small base stations. In the case of the static subchannel, a joint resource allocation and computation offloading scheme based on Lyapunov optimization and Simulated Annealing Genetic Algorithm (SAGA) is put forward. As for the dynamic subchannel, the master-and-slave model is adopted and solved by SAGA and Sequential Quadratic Programming (SQP) method. At last, the effectiveness of the proposed schemes is verified through several simulations.

A Transcoding Task Offloading and Routing Decision-Making Scheme in Live Transmission Architecture Based on Computing Power Network

In recent years, there has been a significant increase in the demand for high-bit-rate live broadcast services, which has led to the widespread use of edge transcoding technology. Edge transcoding can effectively reduce the throughput of streaming media transmission, making it a popular and extensively researched technology. However, due to the real-time requirements of live broadcasting, the edge server needs to have the sufficient computing power to ensure low-latency calculations, which makes computing power allocation and traffic distribution become quite difficult. Inspired by the real-time and flexible computing power scheduling ability of the Computing Power Network, this paper explores reasonable edge task offloading and efficient traffic routing path planning to ensure overall low latency. This paper proposes a live stream transmission architecture based on the computing power network to solve the problems mentioned above to some degree. The paper first models the computing power network in the scene and then designs a task offloading algorithm based on Deep Reinforcement Learning (DQN) to determine the device for executing the computing task. Furthermore, a hybrid Simulated Annealing Genetic Algorithm (SAGA) is proposed for routing decisions. The effectiveness and superiority of the scheme are validated through simulation experiments.

A hybrid optimization algorithm for gate locations in the liquid composite molding process

It is costly to optimize the location of multiple injection gates through a trial and error-based method in the liquid composite molding, even though there are high fidelity physics-based numerical models. A hybrid optimization method called the Simulated Annealing Genetic Algorithm is proposed in this article, which uses the genetic algorithm to provide a global search for a predetermined time and then is further improved by the simulated annealing algorithm. The optimization results of multiple injection gates show that the number of convergence iterations using the Simulated Annealing Genetic Algorithm is less than that using the genetic algorithm, and the phenomenon becomes more obvious as the number of injection gates increases. The case shows that the Simulated Annealing Genetic Algorithm can solve the multiple injection gate configuration problems of highly anisotropic laminates without extra work. The optimization results are in good agreement with the experimental results.