In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed init...In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.展开更多
The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and wi...The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.展开更多
In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation fa...In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.展开更多
Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their int...Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their integration provides an opportunity for improved search performance.However,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only.Differing from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated way.Specifically,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality.Guided by these exemplars,SBAGO improves both BA’s efficiency and global search capability.We evaluate this approach by using 29 widely-adopted problems from four test suites.SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems.Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.展开更多
Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively ...Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.展开更多
Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions var...Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.展开更多
This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigat...This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.展开更多
This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity fa...This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.展开更多
This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal ...This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.展开更多
To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic ...To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.展开更多
Microperforated panels(MPP)are widely used in noise control applications due to their excellent sound absorption performance.However,traditional single-layer MPPs suffer from a narrow sound absorption bandwidth,making...Microperforated panels(MPP)are widely used in noise control applications due to their excellent sound absorption performance.However,traditional single-layer MPPs suffer from a narrow sound absorption bandwidth,making it difficult to meet the demands for broadband sound absorption.To address this limitation,this study proposes a design approach for double-layer MPPs optimized using a genetic algorithm(GA).By optimizing structural parameters such as perforation diameter,panel thickness,perforation ratio,and cavity depth,the sound absorption performance of the double-layer MPP is significantly enhanced.The results demonstrate that the optimized double-layer MPP achieves an average sound absorption coefficient of 0.71 across the 100-5000 Hz frequency range,with a peak absorption coefficient exceeding 0.8 at 500 Hz,outperforming conventional sound-absorbing products of the same category.展开更多
The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The ...The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is f...Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is fabricated,the nonlinear wavefront it generates is fixed,offering little flexibility.This limitation often necessitates the fabrication of different metasurfaces for different wavefronts,which is both time-consuming and inefficient.To address this,we combine evolutionary algorithms with spatial light modulators(SLMs)to dynamically control wavefronts using a single metasurface,reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment.We demonstrate this approach by introducing a genetic algorithm(GA)to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation(THG)in a silicon metasurface.The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions,thereby significantly boosting THG emission at resonant positions.Additionally,the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM.This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints.Our approach paves the way for self-optimized nonlinear wavefront shaping,advancing optical computation and information processing techniques.展开更多
Radio antenna arrays have many advantages for astronomical observations,such as high resolution,high sensitivity,multi-target simultaneous observation,and flexible beam formation.Problems surrounding key indices,such ...Radio antenna arrays have many advantages for astronomical observations,such as high resolution,high sensitivity,multi-target simultaneous observation,and flexible beam formation.Problems surrounding key indices,such as sensitivity enhancement,scanning range extension,and sidelobe level suppression,need to be solved urgently.Here,we propose a sparse optimization scheme based on a genetic algorithm for a 64-array element planar radio antenna array.As optimization targets for the iterative process of the genetic algorithm,we use the maximum sidelobe levels and beamwidth of multiple cross-section patterns that pass through the main beam in three-dimensions,with the maximum sidelobe levels of the patterns at several different scanning angles.Element positions are adjusted for iterations,to select the optimal array configuration.Following sparse layout optimization,the simulated 64-element planar radio antenna array shows that the maximum sidelobe level decreases by 1.79 dB,and the beamwidth narrows by 3°.Within the scan range of±30°,after sparse array optimization,all sidelobe levels decrease,and all beamwidths narrow.This performance improvement can potentially enhance the sensitivity and spatial resolution of radio telescope systems.展开更多
The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensur...The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.展开更多
Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument f...Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument for antenna array thinning.The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level(SLL)while maintaining the half-power beamwidth(HPBW)of a full linear antenna array.Based on results from existing papers in the field and known approaches to antenna array thinning,a classification of thinning types is introduced.The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL.The effect of thinning coefficient on main directional pattern characteristics,such as peak SLL and HPBW,is discussed for a number of amplitude distributions.展开更多
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of...The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.展开更多
Airports around the world commonly face challenges in managing airport slot allocation.Effective management of limited slot resources by civil aviation authority often requires redistributing requested slots among air...Airports around the world commonly face challenges in managing airport slot allocation.Effective management of limited slot resources by civil aviation authority often requires redistributing requested slots among airlines.The allocation process must operate within the prescribed capacity limits of the airport while adhering to established priorities and regulations.Additionally,ensuring market fairness is a key objective,as the value of airport slots plays a significant role in the adjustment process.This transforms the traditional time-shift-based problem into a complex multi-objective optimization problem.Addressing such complications is of significant importance to airlines,airports,and passengers alike.Due to the complexity of fairness metrics,traditional integer programming models encounter difficulties in finding effective solutions.This study proposes a neighborhood search strategy to tackle the single airport slot allocation,making it adaptable to both static and rolling capacity scenarios.Two Genetic Algorithms(GAs)are introduced,corresponding to time adjustment and sequence adjustment strategies,respectively.The GA based on the time adjustment strategy demonstrates high robustness,while the sequence adjustment strategy builds upon this GA to develop a simple heuristic algorithm that offers rapid convergence.Case studies conducted at seven airports in China confirm that all three algorithms yield high-quality adjustment solutions suitable for the majority of applications.Further,Pareto analysis reveals that these algorithms effectively balance the adjustment shifts and fairness metrics,demonstrating high practical value and broad applicability.展开更多
文摘In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.
基金Project(51090385) supported by the Major Program of National Natural Science Foundation of ChinaProject(2011IB001) supported by Yunnan Provincial Science and Technology Program,China+1 种基金Project(2012DFA70570) supported by the International Science & Technology Cooperation Program of ChinaProject(2011IA004) supported by the Yunnan Provincial International Cooperative Program,China
文摘The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.
基金Supported by the Major State Basic Research Development Program of China (2012CB720500)the National Natural Science Foundation of China (Key Program: U1162202)+1 种基金the National Natural Science Foundation of China (General Program:61174118)Shanghai Leading Academic Discipline Project (B504)
文摘In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.
基金This work was supported in part by the Fundamental Research Funds for the Central Universities(YWF-22-L-1203)the National Natural Science Foundation of China(62173013,62073005)+1 种基金the National Key Research and Development Program of China(2020YFB1712203)U.S.National Science Foundation(CCF-0939370,CCF-1908308).
文摘Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their integration provides an opportunity for improved search performance.However,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only.Differing from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated way.Specifically,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality.Guided by these exemplars,SBAGO improves both BA’s efficiency and global search capability.We evaluate this approach by using 29 widely-adopted problems from four test suites.SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems.Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.
文摘Bicycle sharing scheduling is a complex mathematical optimization problem, and it is challenging to design a general algorithm to solve it well due to the uncertainty of its influencing factors. This paper creatively establishes a new mathematical model to determine the appropriate number of vehicles to be placed at each placement point by calculating the traffic weights of the placement points and optimizes the hyperparameters in the algorithm by adaptive quantum genetic algorithm, and at the same time combines the network flow algorithm in graph theory to calculate the most suitable scheduling scheme for shared bicycles by establishing the minimum cost maximum flow network. Through experimental validation, the network flow-based algorithm proposed in this paper allows for a more convenient calculation of the daily bike-sharing scheduling scheme compared to previous algorithms. An adaptive quantum genetic algorithm optimizes the hyperparameters appearing in the algorithm. The experimental results show that the algorithm achieves good results as the transportation cost is only 1/15th of the GA algorithm and 1/9th of the QGA algorithm.
文摘Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.
文摘This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.
文摘This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.
文摘This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.
文摘To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.
文摘Microperforated panels(MPP)are widely used in noise control applications due to their excellent sound absorption performance.However,traditional single-layer MPPs suffer from a narrow sound absorption bandwidth,making it difficult to meet the demands for broadband sound absorption.To address this limitation,this study proposes a design approach for double-layer MPPs optimized using a genetic algorithm(GA).By optimizing structural parameters such as perforation diameter,panel thickness,perforation ratio,and cavity depth,the sound absorption performance of the double-layer MPP is significantly enhanced.The results demonstrate that the optimized double-layer MPP achieves an average sound absorption coefficient of 0.71 across the 100-5000 Hz frequency range,with a peak absorption coefficient exceeding 0.8 at 500 Hz,outperforming conventional sound-absorbing products of the same category.
文摘The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
基金support from the Biotechnology and Biological Council Doctoral Training Programme(BBSRC DTP)the support from the Royal Society and Wolfson Foundation(RSWF\FT\191022).
文摘Nonlinear wavefront shaping is crucial for advancing optical technologies,enabling applications in optical computation,information processing,and imaging.However,a significant challenge is that once a metasurface is fabricated,the nonlinear wavefront it generates is fixed,offering little flexibility.This limitation often necessitates the fabrication of different metasurfaces for different wavefronts,which is both time-consuming and inefficient.To address this,we combine evolutionary algorithms with spatial light modulators(SLMs)to dynamically control wavefronts using a single metasurface,reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment.We demonstrate this approach by introducing a genetic algorithm(GA)to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation(THG)in a silicon metasurface.The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions,thereby significantly boosting THG emission at resonant positions.Additionally,the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM.This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints.Our approach paves the way for self-optimized nonlinear wavefront shaping,advancing optical computation and information processing techniques.
基金supported by the Ministry of Science and Technology SKA Special Project(2020SKA0110202)the Special Project on Building a Science and Technology Innovation Center for South and Southeast Asia–International Joint Innovation Platform in Yunnan Province:"Yunnan Sino-Malaysian International Joint Laboratory of HF-VHF Advanced Radio Astronomy Technology"(202303AP140003)+4 种基金the National Natural Science Foundation of China (NSFC) Joint Fund for Astronomy (JFA) incubator program (U2031133)the International Partnership Program Project of the International Cooperation Bureau of the Chinese Academy of Sciences:"Belt and Road"Cooperation (114A11KYSB20200001)the Kunming Foreign (International) Cooperation Base Program:"Yunnan Observatory of the Chinese Academy of Sciences-University of Malaya Joint R&D Cooperation Base for Advanced Radio Astronomy Technology"(GHJD-2021022)the China-Malaysia Collaborative Research on Space Remote Sensing and Radio Astronomy Observation of Space Weather at Low and Middle Latitudes under the Key Special Project of the State Key R&D Program of the Ministry of Science and Technology for International Cooperation in Science,Technology and Innovation among Governments (2022YFE0140000)the High-precision calibration method for low-frequency radio interferometric arrays for the SKA project of the Ministry of Science and Technology(2020SKA0110300).
文摘Radio antenna arrays have many advantages for astronomical observations,such as high resolution,high sensitivity,multi-target simultaneous observation,and flexible beam formation.Problems surrounding key indices,such as sensitivity enhancement,scanning range extension,and sidelobe level suppression,need to be solved urgently.Here,we propose a sparse optimization scheme based on a genetic algorithm for a 64-array element planar radio antenna array.As optimization targets for the iterative process of the genetic algorithm,we use the maximum sidelobe levels and beamwidth of multiple cross-section patterns that pass through the main beam in three-dimensions,with the maximum sidelobe levels of the patterns at several different scanning angles.Element positions are adjusted for iterations,to select the optimal array configuration.Following sparse layout optimization,the simulated 64-element planar radio antenna array shows that the maximum sidelobe level decreases by 1.79 dB,and the beamwidth narrows by 3°.Within the scan range of±30°,after sparse array optimization,all sidelobe levels decrease,and all beamwidths narrow.This performance improvement can potentially enhance the sensitivity and spatial resolution of radio telescope systems.
文摘The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.
文摘Thinning of antenna arrays has been a popular topic for the last several decades.With increasing computational power,this optimization task acquired a new hue.This paper suggests a genetic algorithm as an instrument for antenna array thinning.The algorithm with a deliberately chosen fitness function allows synthesizing thinned linear antenna arrays with low peak sidelobe level(SLL)while maintaining the half-power beamwidth(HPBW)of a full linear antenna array.Based on results from existing papers in the field and known approaches to antenna array thinning,a classification of thinning types is introduced.The optimal thinning type for a linear thinned antenna array is determined on the basis of a maximum attainable SLL.The effect of thinning coefficient on main directional pattern characteristics,such as peak SLL and HPBW,is discussed for a number of amplitude distributions.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
文摘The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms(GA)with machine learning-based linear regression,aiming to improve the accuracy and efficiency of geostatistical analysis,particularly in mineral exploration.The study combines GA and machine learning to optimise variogram parameters,including range,sill,and nugget,by minimising the root mean square error(RMSE)and maximising the coefficient of determination(R^(2)).The experimental variograms were computed and modelled using theoretical models,followed by optimisation via evolutionary algorithms.The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon,covering 141 data points.Sequential Gaussian Simulations(SGS)were employed for predictive mapping to validate simulated results against true values.Key findings show variograms with ranges between 24.71 km and 49.77 km,opti-mised RMSE and R^(2) values of 11.21 mGal^(2) and 0.969,respectively,after 42 generations of GA optimisation.Predictive mapping using SGS demonstrated that simulated values closely matched true values,with the simu-lated mean at 21.75 mGal compared to the true mean of 25.16 mGal,and variances of 465.70 mGal^(2) and 555.28 mGal^(2),respectively.The results confirmed spatial variability and anisotropies in the N170-N210 directions,consistent with prior studies.This work presents a novel integration of GA and machine learning for variogram modelling,offering an automated,efficient approach to parameter estimation.The methodology significantly enhances predictive geostatistical models,contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.
基金supported in part by the National Natural Science Foundation of China(Nos.62167003,52302421)in part by the Diversified Investment Fund of Tianjin,China(No.23JCQNJC00210)。
文摘Airports around the world commonly face challenges in managing airport slot allocation.Effective management of limited slot resources by civil aviation authority often requires redistributing requested slots among airlines.The allocation process must operate within the prescribed capacity limits of the airport while adhering to established priorities and regulations.Additionally,ensuring market fairness is a key objective,as the value of airport slots plays a significant role in the adjustment process.This transforms the traditional time-shift-based problem into a complex multi-objective optimization problem.Addressing such complications is of significant importance to airlines,airports,and passengers alike.Due to the complexity of fairness metrics,traditional integer programming models encounter difficulties in finding effective solutions.This study proposes a neighborhood search strategy to tackle the single airport slot allocation,making it adaptable to both static and rolling capacity scenarios.Two Genetic Algorithms(GAs)are introduced,corresponding to time adjustment and sequence adjustment strategies,respectively.The GA based on the time adjustment strategy demonstrates high robustness,while the sequence adjustment strategy builds upon this GA to develop a simple heuristic algorithm that offers rapid convergence.Case studies conducted at seven airports in China confirm that all three algorithms yield high-quality adjustment solutions suitable for the majority of applications.Further,Pareto analysis reveals that these algorithms effectively balance the adjustment shifts and fairness metrics,demonstrating high practical value and broad applicability.
