Genetic Simulated Annealing Algorithm Research Articles

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Systematic Analysis and Research on Integrated Circuit Design Optimization

After decades of development of integrated circuits, there has been great progress in all aspects. These advances are reflected in the optimization of manufacturing processes and circuits. When designing a circuit, considering the production cost and the performance of the circuit, the optimal design of the circuit is always a very important link. In this paper, two kinds of optimization ideas are introduced. One is from the point of view of the physical design of the circuit, two methods are introduced, which are adjusting the input power and reducing the circuit device. The other is to optimize the circuit from the point of view of algorithm. Genetic algorithm and simulated annealing algorithm are introduced. After optimizing the circuit, the circuit energy consumption is reduced, the circuit delay is reduced, the circuit runs faster, and the overall performance is also improved. In the future, with the development of related technologies, the optimization of integrated circuits will be faster.

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.

Optimization of the energy management system in hybrid electric vehicles considering cabin temperature

The air conditioning (AC) system provides passengers with a comfortable temperature environment and is one of the main energy consumers in modern plug-in hybrid electric vehicles (PHEV). However, the AC system is often overlooked when designing the energy management system (EMS) for PHEVs. To improve the energy efficiency of PHEVs, this paper integrates the Genetic Simulated Annealing Algorithm (GASA) with fuzzy control to propose an energy management system for PHEVs that considers the internal temperature of the vehicle. First, considering the uncertainty of the vehicle's driving environment, an optimized Interval Type-2 Fuzzy Controller (IT2-FC) was designed for real-time torque distribution. Second, an iteratively modified genetic simulated annealing algorithm was used to optimize control parameters, correcting the defect of genetic algorithms tending to get trapped in local optima and lingering around optimal positions. Then, considering the energy consumption of the AC system, a vehicle-integrated thermal management model oriented towards control was established. Lastly, the performance of the proposed EMS was discussed. The results show that, compared to the rule-based methods, the proposed EMS reduces fuel consumption by 17.77% and 17.727% in cooling and heating modes, respectively, and compared to the A-ECMS methods, it reduces fuel consumption by 7.38% and 6.31% in cooling and heating modes, respectively. At the same time, the proposed EMS ensures thermal comfort in the cabin. In cooling mode, the traditional rule-based strategy failed to maintain the cabin temperature within the preset range, while the proposed EMS showed significant improvement. In heating mode, the EMS reaches and maintains the cabin's set temperature 35% faster than the rule-based strategy, and 8.75% faster than the A-ECMS strategy.

Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

Abstract Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

Coherent Doppler lidar wind retrieval for a typhoon based on the genetic simulated annealing algorithm

Coherent Doppler lidar wind retrieval for a typhoon based on the genetic simulated annealing algorithm

The Impact of Big Data Analytics Capabilities on the Diversification of E-Commerce Firms

Abstract With the arrival of the big data era, e-commerce enterprises are facing more and more competition and market changes. A fuzzy clustering model is created by combining fuzzy set theory and fuzzy logic with a clustering algorithm in this paper. Then, for the shortcomings of the fuzzy C-mean clustering algorithm, the genetic simulated annealing algorithm is used to weight the data points, the fuzzy C-mean clustering model is improved, and a fuzzy clustering algorithm based on the genetic simulated annealing optimization is proposed. By using this algorithm, e-commerce enterprises’ diversification is being analyzed. The results show that the mean and standard deviation of each supply chain inventory, production orders, supplier ordering, and platform ordering quantity after optimization are reduced to different degrees, some of which reach more than 60%. Stabilizing the development of e-commerce enterprises can be achieved by reducing supply chain inventory and order quantity. E-commerce enterprises that excel in their enterprise production quality (0.741), credibility (0.748), and product sales competitiveness (0.726) are more competitive. E-commerce enterprises can utilize the ability of big data analysis to achieve diversification.

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

Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989–2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

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.

Simulation research on breakdown diagnosis based on least squares support vector machine optimized by simulated annealing genetic algorithm

The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced.

A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

Integrated optimization and engineering application for disassembly line balancing problem with preventive maintenance

The current research on the disassembly line balancing (DLB) problem has not yet considered the preventive maintenance of workstation equipment. The lack of preventive maintenance can easily lead to sudden failure of the disassembly line equipment, which will affect the disassembly efficiency. Preventive maintenance is planned maintenance, which can effectively reduce the probability of equipment failure in actual disassembly line engineering applications. This study integrates preventive maintenance into the DLB. The objectives focus on optimizing both cycle times under the regular DLB scenario and the preventive maintenance DLB scenario, and the number of task adjustments between the two scenarios. A mixed integer linear programming model integrating preventive maintenance is established. Then an improved genetic simulated annealing (IGSA) algorithm based on the disassembly characteristics is designed. The exact solver and the proposed IGSA are used to solve a small-scale case simultaneously to verify the correctness of the model and algorithm. The performance of the proposed IGSA is verified by comparing the solution results of 21 benchmarks. Finally, the proposed model and algorithm are applied to a microwave disassembly line as the engineering application case. The results show that the integrated optimization can ensure the disassembly efficiency of the regular disassembly scenario, and can fully utilize workstations of unperformed maintenance during the preventive maintenance scenario. This can also effectively improve workstation utilization and reduce time costs.

Rapid determination of rice protein content using near-infrared spectroscopy coupled with feature wavelength selection

Protein content is a key indicator for rice nutritional value, and its accurate determination has become a necessary tool for rice quality evaluation and breeding. To solve the shortcomings of slow and costly testing by traditional methods, a rapid detection model of rice protein content was constructed based on near-infrared spectroscopy coupled with feature wavelength selection. The backward interval partial least squares (BiPLS) was combined with the genetic simulated annealing algorithm (GSA) and the simulated annealing binary particle swarm optimization algorithm (SABPSO), respectively, to construct BiPLS-GSA and BiPLS-SABPSO for selecting protein feature wavelengths, thereby establishing a corresponding partial least squares quantitative calibration model. Among them, the regression model established using feature wavelengths selected by BiPLS-SABPSO had the best performance. The determination coefficient of the model for the validation set and the independent test set were 0.949 and 0.956, the root mean square error were 0.174% and 0.214%, the relative root mean square error were 2.621% and 3.118%, and the residual predictive deviation being 4.235 and 4.500, respectively, which could meet the requirement for rapid and accurate determination of rice protein content. The combination of BiPLS-SABPSO and near-infrared spectroscopy has developed into a new method to achieve fast and reliable determination of rice protein content, providing an alternative strategy for rapid qualitative detection of related agricultural products.

The improved quantum genetic algorithm with its application in fault diagnosis

In this paper, by analyzing the characteristics of the simulated annealing algorithm (SA) and the real double-chain coding target gradient quantum genetic algorithm (DCQGA), the real double-chain coding target gradient quantum genetic simulated annealing algorithm (DCQGSAA) is proposed. Because the performance of LSSVR is extraordinarily sensitive to its key parameters, the proposed algorithm is used to optimize these parameters, then a hybrid non-parametric prediction model is put forward. This model is used in fault prediction of liquid rocket engine thrust. The simulation results show that the proposed model is effective for small samples and multi-dimensional fault prediction.

Decomposition and approximate dynamic programming approach to optimization of train timetable and skip-stop plan for metro networks

Carefully coordinating train timetables of different operating lines can help reduce transfer delays, which in turn reduces station crowding and improves overall service quality. This paper explores the optimization to train timetable and skip-stop plans that aims to minimize the total passenger waiting time and station crowding. The problem is formulated as a mixed-integer non-linear programming model. To effectively address the complexity of the model, a decomposition and approximate dynamic programming approach is designed to convert the original network-level problem into a series of small-scale subproblems, one for each operating line, to be solved quickly in a distributed manner. The effectiveness and practicability of the model and algorithm are demonstrated on two case networks: a small-scale synthetic network of three metro lines and a real-world network based on Beijing metro. The computational results illustrate that the proposed strategy to generate train timetables and skip-stop plans can effectively reduce passenger waiting time and station crowing. The proposed decomposition and approximate dynamic programming approach is also shown to perform more efficiently than traditional heuristic algorithms, such as genetic algorithm and simulated annealing algorithm for large-scale networks.

Multi-Traveler Salesman Problem for Unmanned Vehicles: Optimization through Improved Hopfield Neural Network

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles’ maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread.

Modeling of farmers’ production performance portrait based on GASA-WFCM clustering

To identify the typical characteristics of farmers with different production performance, this paper constructs a farmer portrait model from the perspective of production performance segmentation. Firstly, we constructed the evaluation index system of production performance and quantified grape farmers’ production performance. Then, an optimized clustering algorithm (GASA-WFCM) was proposed to cluster the grape farmers based on their production performance, which was based on fuzzy C-mean algorithm, and incorporating the genetic simulated annealing algorithm to overcome the problem of sensitive initial clustering centers and tendency to fall into local optimum, and the ReliefF algorithm was also integrated to strengthen the contribution ability of effective features to clustering. Finally, we designed a labeling system and feature transformation quantification method, and combined with the visualization effects to realize the description of grape farmers’ portraits. The results of models training indicated that the convergence speed and division accuracy of the optimized clustering algorithm were significantly improved. By comparing the clustering results on the typical UCI datasets, it was demonstrated that GASA-WFCM algorithm improved the accuracy, NMI and ARI by 3.0%, 7.2% and 6.1%, respectively. Moreover, the segmentation method was applied in grape farmers dataset, and the findings revealed that Chinese fresh grape farmers may be clustered into three groups, namely high-yield-driven grape farmers, high-input-driven grape farmers and high -income grape farmers, respectively. This paper provided a set of methods of users clustering analysis and portrait from a theoretical point of view. The findings and suggestions can help provide accurate and focused decision support for improving the effect of agricultural technology promotion and farmers’ production performance.

A two-stage decision-support system for floating debris collection in reservoir areas

The accumulation of floating debris is one of the main challenges of water conservancy projects, which may pollute the vulnerable ecosystem of the reservoir area and impose significant risks on waterway transportation and dam operations. Due to the dynamicity and uncertainty caused by water flow, the collection of floating debris is much more complicated than on-land waste collection. In this paper, we propose a two-stage decision-support system to optimize the task allocation and routing decisions for floating debris collection in the reservoir area, where the first stage is proactive planning based on historical/observed data and the second stage is reactive planning based on real-time data. The primary objective is to minimize the total collection cost while simultaneously ensuring the accumulation areas with high risks are prioritized in the daily collection plan, and both genetic algorithm and simulated annealing algorithm are used to solve the optimization problems. The proposed method is validated with a real-world case study at Wushan County in the Three Gorges Reservoir area. The computational results show that the level of time-dependent penalty cost on service priority, the types of collection ships, and the number and locations of unloading points are important influencing factors to the cost and responsiveness. Furthermore, the proposed two-stage decision-support system can help effectively optimize the operational planning of floating debris collection in reservoir areas.