Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algori...Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.展开更多
Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic opt...Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best.To avoid BES collapse at local optima,inspired by the fact that the volume of the sphere is the largest when the surface area is certain,an improved bald eagle search optimization algorithm(INMBES)integrating the random shrinkage mechanism of the sphere is proposed.Firstly,the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population.Secondly,the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES.The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES.The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10,30,50,and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third.The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks first and has achieved satisfactory accuracy in solving practical problems.展开更多
The Unit Commitment Problem(UCP)corresponds to the planning of power generation schedules.The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet...The Unit Commitment Problem(UCP)corresponds to the planning of power generation schedules.The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown times.In this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer functions.In addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and window-mutation.This variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and 100-unit.For comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization techniques.In other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit problems.Furthermore,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment problems.The Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the best.In conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.展开更多
Background:Mercury(Hg) and methylmercury are widely considered significant issues for wildlife,and in particular,piscivorous birds due to their widespread availability and neurotoxic properties.Whereas a substantial n...Background:Mercury(Hg) and methylmercury are widely considered significant issues for wildlife,and in particular,piscivorous birds due to their widespread availability and neurotoxic properties.Whereas a substantial number of studies of Hg contamination of Bald Eagles(Haliaeetus leucocephalus) have been conducted throughout the east coast of the United States,little has been done that directly addresses Hg contamination in Bald Eagles in Virginia,particularly the inland population.Methods:We collected blood and feather samples from nestling Bald Eagles in the coastal plain,piedmont,and western regions of Virginia in an effort to determine which areas of the state were more likely to contain populations showing evidence of Hg toxicity.We analyzed the samples for total Hg using a Milestone DMA-80.Results:Samples collected from individuals located in the coastal region exhibited low concentrations of Hg compared to those further inland located on freshwater rivers and reservoirs.Samples collected from the inland population exhibited levels in some areas that are approaching what may be considered to be sub-lethal to avian health(blood:mean 0.324 mg/kg,SE = 0.13,range = 0.06-0.97 mg/kg;feather:mean = 8.433 mg/kg,SE = 0.3,range = 3.811-21.14 mg/kg).Conclusions:Even after accounting for known point-sources of Hg,the inland eagle population in Virginia is susceptible to concentrations of Hg that are significantly higher than their coastal counterparts.Moreover,several locations besides those currently known to be impacted by point-sources are exhibiting concentrations that are approaching a sub-lethal level.展开更多
In this study,a bald eagle optimizer(BEO)is used to get optimal parameters of the fractional-order proportional-integral-derivative(FOPID)controller for load frequency control(LFC).SinceBEOtakes only a very short time...In this study,a bald eagle optimizer(BEO)is used to get optimal parameters of the fractional-order proportional-integral-derivative(FOPID)controller for load frequency control(LFC).SinceBEOtakes only a very short time in finding the optimal solution,it is selected for designing the FOPID controller that improves the system stability and maintains the frequency within a satisfactory range at different loads.Simulations and demonstrations are carried out using MATLAB-R2020b.The performance of the BEOFOPID controller is evaluated using a two-zone interlinked power system at different loads and under uncertainty of wind and solar energies.The robustness of the BEO-FOPID controller is examined by testing its performance under varying system time constants.The results obtained by the BEOFOPID controller are compared with those obtained by BEO-PID and PID controllers based on recent metaheuristics optimization algorithms,namely the sine-cosine approach,Jaya approach,grey wolf optimizer,genetic algorithm,bacteria foraging optimizer,and equilibrium optimization algorithm.The results confirm that the BEO-FOPID controller obtains the finest result,with the lowest frequency deviation.The results also confirm that the BEOFOPID controller is stable and robust at different loads,under varying system time constants,and under uncertainty of wind and solar energies.展开更多
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta...The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.展开更多
This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,mainte...This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,maintenance and operating expenses of energy storage systems,diesel generator operational costs,typical daily load profiles,and power balance constraints.A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows.The Bald Eagle Search(BES)meta-heuristic is adopted to solve the resulting constrained optimization problem.Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing power backflow and maintaining secure operation of the distribution network.展开更多
为了进一步提升主动配电网(active distribution network,ADN)的经济性,以ADN日最低运行成本为目标函数,同时考虑价格型需求响应对ADN调度的影响,构建了主动配电网调度模型。利用Tent混沌方程和柯西突变算子对秃鹰搜索(bald eagle searc...为了进一步提升主动配电网(active distribution network,ADN)的经济性,以ADN日最低运行成本为目标函数,同时考虑价格型需求响应对ADN调度的影响,构建了主动配电网调度模型。利用Tent混沌方程和柯西突变算子对秃鹰搜索(bald eagle search,BES)算法进行改进,使改进秃鹰搜索(improved bald eagle search,IBES)算法的优化效果得到改善。采用IBES算法对ADN调度模型进行求解,并利用改进IEEE节点系统搭建不同场景进行仿真分析。仿真结果表明,考虑需求响应时,负荷曲线峰谷差更小,需求响应使部分峰值负荷转移至低谷时段,实现了对负荷的削峰填谷,在需求响应的作用下,ADN日最低运行成本更小,经济性更好。展开更多
Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinio...Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approach-Aiming at the complex and nonlinear characteristics of the network public opinion,considering the accuracy and stability of the applicable model,a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network(BES-RBF)is proposed.Empirical research is conducted with Baidu indexes such as“COVID-19”,“Winter Olympic Games”,“The 100th Anniversary of the Founding of the Party”and“Aerospace”as samples of network public opinion.Findings-The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information,has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/value-A method for optimizing the central value,weight,width and other parameters of the radial basis function neural network with the bald eagle algorithm is given,and it is applied to network public opinion trend prediction.The example verifies that the prediction algorithm has higher accuracy and better stability.展开更多
基金supported by the Research on theKey Technology of Damage Identification Method of Dam Concrete Structure based on Transformer Image Processing(242102521031)the project Research on Situational Awareness and Behavior Anomaly Prediction of Social Media Based on Multimodal Time Series Graph(232102520004)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B520019).
文摘Image enhancement utilizes intensity transformation functions to maximize the information content of enhanced images.This paper approaches the topic as an optimization problem and uses the bald eagle search(BES)algorithm to achieve optimal results.In our proposed model,gamma correction and Retinex address color cast issues and enhance image edges and details.The final enhanced image is obtained through color balancing.The BES algorithm seeks the optimal solution through the selection,search,and swooping stages.However,it is prone to getting stuck in local optima and converges slowly.To overcome these limitations,we propose an improved BES algorithm(ABES)with enhanced population learning,position updates,and control parameters.ABES is employed to optimize the core parameters of gamma correction and Retinex to improve image quality,and the maximization of information entropy is utilized as the objective function.Real benchmark images are collected to validate its performance.Experimental results demonstrate that ABES outperforms the existing image enhancement methods,including the flower pollination algorithm,the chimp optimization algorithm,particle swarm optimization,and BES,in terms of information entropy,peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and patch-based contrast quality index(PCQI).ABES demonstrates superior performance both qualitatively and quantitatively,and it helps enhance prominent features and contrast in the images while maintaining the natural appearance of the original images.
基金supported by the National Natural Science Foundation of China No.61976176.
文摘Over the last two decades,stochastic optimization algorithms have proved to be a very promising approach to solving a variety of complex optimization problems.Bald eagle search optimization(BES)as a new stochastic optimization algorithm with fast convergence speed has the ability of prominent optimization and the defect of collapsing in the local best.To avoid BES collapse at local optima,inspired by the fact that the volume of the sphere is the largest when the surface area is certain,an improved bald eagle search optimization algorithm(INMBES)integrating the random shrinkage mechanism of the sphere is proposed.Firstly,the INMBES embeds spherical coordinates to design a more accurate parameter update method to modify the coverage and dispersion of the population.Secondly,the population splits into elite and non-elite groups and the Bernoulli chaos is applied to elite group to tap around potential solutions of the INMBES.The non-elite group is redistributed again and the Nelder-Mead simplex strategy is applied to each group to accelerate the evolution of the worst individual and the convergence process of the INMBES.The results of Friedman and Wilcoxon rank sum tests of CEC2017 in 10,30,50,and 100 dimensions numerical optimization confirm that the INMBES has superior performance in convergence accuracy and avoiding falling into local optimization compared with other potential improved algorithms but inferior to the champion algorithm and ranking third.The three engineering constraint optimization problems and 26 real world problems and the problem of extracting the best feature subset by encapsulated feature selection method verify that the INMBES’s performance ranks first and has achieved satisfactory accuracy in solving practical problems.
文摘The Unit Commitment Problem(UCP)corresponds to the planning of power generation schedules.The objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown times.In this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer functions.In addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and window-mutation.This variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and 100-unit.For comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization techniques.In other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit problems.Furthermore,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment problems.The Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the best.In conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.
基金supported under the US Environmental Protection Agency-Science to Acheive Results(STAR) Fellowship Program#F6C20816the Virginia Dept.of Game and Inland Fisheries
文摘Background:Mercury(Hg) and methylmercury are widely considered significant issues for wildlife,and in particular,piscivorous birds due to their widespread availability and neurotoxic properties.Whereas a substantial number of studies of Hg contamination of Bald Eagles(Haliaeetus leucocephalus) have been conducted throughout the east coast of the United States,little has been done that directly addresses Hg contamination in Bald Eagles in Virginia,particularly the inland population.Methods:We collected blood and feather samples from nestling Bald Eagles in the coastal plain,piedmont,and western regions of Virginia in an effort to determine which areas of the state were more likely to contain populations showing evidence of Hg toxicity.We analyzed the samples for total Hg using a Milestone DMA-80.Results:Samples collected from individuals located in the coastal region exhibited low concentrations of Hg compared to those further inland located on freshwater rivers and reservoirs.Samples collected from the inland population exhibited levels in some areas that are approaching what may be considered to be sub-lethal to avian health(blood:mean 0.324 mg/kg,SE = 0.13,range = 0.06-0.97 mg/kg;feather:mean = 8.433 mg/kg,SE = 0.3,range = 3.811-21.14 mg/kg).Conclusions:Even after accounting for known point-sources of Hg,the inland eagle population in Virginia is susceptible to concentrations of Hg that are significantly higher than their coastal counterparts.Moreover,several locations besides those currently known to be impacted by point-sources are exhibiting concentrations that are approaching a sub-lethal level.
基金This research was funded by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia through the project number“IF_2020_NBU_434”.
文摘In this study,a bald eagle optimizer(BEO)is used to get optimal parameters of the fractional-order proportional-integral-derivative(FOPID)controller for load frequency control(LFC).SinceBEOtakes only a very short time in finding the optimal solution,it is selected for designing the FOPID controller that improves the system stability and maintains the frequency within a satisfactory range at different loads.Simulations and demonstrations are carried out using MATLAB-R2020b.The performance of the BEOFOPID controller is evaluated using a two-zone interlinked power system at different loads and under uncertainty of wind and solar energies.The robustness of the BEO-FOPID controller is examined by testing its performance under varying system time constants.The results obtained by the BEOFOPID controller are compared with those obtained by BEO-PID and PID controllers based on recent metaheuristics optimization algorithms,namely the sine-cosine approach,Jaya approach,grey wolf optimizer,genetic algorithm,bacteria foraging optimizer,and equilibrium optimization algorithm.The results confirm that the BEO-FOPID controller obtains the finest result,with the lowest frequency deviation.The results also confirm that the BEOFOPID controller is stable and robust at different loads,under varying system time constants,and under uncertainty of wind and solar energies.
基金Project of Key Science and Technology of the Henan Province(No.202102310259)Henan Province University Scientific and Technological Innovation Team(No.18IRTSTHN009).
文摘The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
基金the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.(Project No.J2024066).
文摘This paper proposes a cost-optimal energy management strategy for reconfigurable distribution networks with high penetration of renewable generation.The proposed strategy accounts for renewable generation costs,maintenance and operating expenses of energy storage systems,diesel generator operational costs,typical daily load profiles,and power balance constraints.A penalty term for power backflow is incorporated into the objective function to discourage undesirable reverse flows.The Bald Eagle Search(BES)meta-heuristic is adopted to solve the resulting constrained optimization problem.Numerical simulations under multiple load scenarios demonstrate that the proposed method effectively reduces operating cost while preventing power backflow and maintaining secure operation of the distribution network.
文摘为了进一步提升主动配电网(active distribution network,ADN)的经济性,以ADN日最低运行成本为目标函数,同时考虑价格型需求响应对ADN调度的影响,构建了主动配电网调度模型。利用Tent混沌方程和柯西突变算子对秃鹰搜索(bald eagle search,BES)算法进行改进,使改进秃鹰搜索(improved bald eagle search,IBES)算法的优化效果得到改善。采用IBES算法对ADN调度模型进行求解,并利用改进IEEE节点系统搭建不同场景进行仿真分析。仿真结果表明,考虑需求响应时,负荷曲线峰谷差更小,需求响应使部分峰值负荷转移至低谷时段,实现了对负荷的削峰填谷,在需求响应的作用下,ADN日最低运行成本更小,经济性更好。
基金supported in part by the National Natural Science Foundation of China(No.11371130,12071179)Soft science research program of Fujian Province(No.B19085)+3 种基金the project of Education Department of Fujian Province(No.JT180263)the Youth Innovation Fund of Xiamen City(3502Z20206020)the open fund of Key Laboratory of Applied Mathematics of Fujian Province University(Putian University)(No.SX201906)Digital Fujian big data modeling and intelligent computing institute,Pre-Research Fund of Jimei University.
文摘Purpose-In the new era of highly developed Internet information,the prediction of the development trend of network public opinion has a very important reference significance for monitoring and control of public opinion by relevant government departments.Design/methodology/approach-Aiming at the complex and nonlinear characteristics of the network public opinion,considering the accuracy and stability of the applicable model,a network public opinion prediction model based on the bald eagle algorithm optimized radial basis function neural network(BES-RBF)is proposed.Empirical research is conducted with Baidu indexes such as“COVID-19”,“Winter Olympic Games”,“The 100th Anniversary of the Founding of the Party”and“Aerospace”as samples of network public opinion.Findings-The experimental results show that the model proposed in this paper can better describe the development trend of different network public opinion information,has good stability in predictive performance and can provide a good decision-making reference for government public opinion control departments.Originality/value-A method for optimizing the central value,weight,width and other parameters of the radial basis function neural network with the bald eagle algorithm is given,and it is applied to network public opinion trend prediction.The example verifies that the prediction algorithm has higher accuracy and better stability.
