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
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
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
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
针对区域洪水灾害恢复力评价方法较为薄弱及其驱动机制不明的问题,构建自然、经济、社会和技术4个维度的洪水灾害恢复力评价指标体系,建立秃鹰搜索算法改进投影寻踪模型(Projection Pursuit Model Based on Bald Eagle Search Optimizat...针对区域洪水灾害恢复力评价方法较为薄弱及其驱动机制不明的问题,构建自然、经济、社会和技术4个维度的洪水灾害恢复力评价指标体系,建立秃鹰搜索算法改进投影寻踪模型(Projection Pursuit Model Based on Bald Eagle Search Optimization Algorithm,BES-PP)。基于此,利用BES-PP模型对佳木斯市2003-2020年的洪水灾害恢复力进行评估。结果显示:佳木斯市的恢复力水平可分为3个阶段,第一阶段(2003-2006年)恢复力平稳上升但是水平较低,第二阶段(2007-2013年)恢复力水平出现明显波动,第三阶段(2014-2020年)恢复力快速上升并最终达到较高水平。为分析其驱动机制,利用BES-PP模型对各个指标的权重进行分析,结果发现经济维和自然维权重分别达到1.21168和0.93242,其平均权重分别达到0.30292和0.31081,远高于社会维和技术维。表明自然维和经济维的指标对洪水灾害恢复力具有较高程度的影响,应当重视自然及经济因素在规避洪水灾害中的重要作用。为验证BES-PP模型性能,引入了黏菌优化算法改进投影寻踪模型(Projection Pursuit Model Based on Slime Mould Algorithm,SMA-PP)和鲸鱼优化算法改进投影寻踪模型(Projection Pursuit Model Based on Whale Optimization Algorithm,WOA-PP)进行对比分析。结果显示:BES-PP模型在性能上具有较为明显的优势。此外,采用序号总和理论分析BES-PP、SMA-PP和WOA-PP的评价结果发现,BES-PP模型的评价结果更具合理性。研究成果为佳木斯市防灾减灾能力提升提供了科学依据,同时也为灾害恢复力评估提供了一些有价值的信息。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.
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.
文摘针对区域洪水灾害恢复力评价方法较为薄弱及其驱动机制不明的问题,构建自然、经济、社会和技术4个维度的洪水灾害恢复力评价指标体系,建立秃鹰搜索算法改进投影寻踪模型(Projection Pursuit Model Based on Bald Eagle Search Optimization Algorithm,BES-PP)。基于此,利用BES-PP模型对佳木斯市2003-2020年的洪水灾害恢复力进行评估。结果显示:佳木斯市的恢复力水平可分为3个阶段,第一阶段(2003-2006年)恢复力平稳上升但是水平较低,第二阶段(2007-2013年)恢复力水平出现明显波动,第三阶段(2014-2020年)恢复力快速上升并最终达到较高水平。为分析其驱动机制,利用BES-PP模型对各个指标的权重进行分析,结果发现经济维和自然维权重分别达到1.21168和0.93242,其平均权重分别达到0.30292和0.31081,远高于社会维和技术维。表明自然维和经济维的指标对洪水灾害恢复力具有较高程度的影响,应当重视自然及经济因素在规避洪水灾害中的重要作用。为验证BES-PP模型性能,引入了黏菌优化算法改进投影寻踪模型(Projection Pursuit Model Based on Slime Mould Algorithm,SMA-PP)和鲸鱼优化算法改进投影寻踪模型(Projection Pursuit Model Based on Whale Optimization Algorithm,WOA-PP)进行对比分析。结果显示:BES-PP模型在性能上具有较为明显的优势。此外,采用序号总和理论分析BES-PP、SMA-PP和WOA-PP的评价结果发现,BES-PP模型的评价结果更具合理性。研究成果为佳木斯市防灾减灾能力提升提供了科学依据,同时也为灾害恢复力评估提供了一些有价值的信息。
