This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow an...This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanis...The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.展开更多
针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改...针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改进,能够更加均匀地在搜索空间得到初始种群解,提高种群多样性;在进行位置更新时,引入减法平均优化器和柯西策略能够扩大搜索范围,从而跳出局部最优;引入黄金正弦策略,不断向最优位置进行搜索,提高了算法的估计精度。将所提算法应用于最大似然DOA估计,实验结果表明:与灰狼优化算法、粒子群算法、正弦余弦算法、光学显微镜算法、蛇鹫优化算法的最大似然DOA估计方法相比,所提算法的最大似然DOA估计具有收敛速度快、估计精度高、运算量小和稳健性更好的性能。展开更多
文摘This paper addresses the shortcomings of the Sparrow and Eagle Optimization Algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle Optimization Algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
基金funded by State Grid Beijing Electric Power Company Technology Project,grant number 520210230004.
文摘The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy integration.However,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES scheduling.To address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the Secretary Bird Optimization Algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response revenue.Case studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.
文摘针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改进,能够更加均匀地在搜索空间得到初始种群解,提高种群多样性;在进行位置更新时,引入减法平均优化器和柯西策略能够扩大搜索范围,从而跳出局部最优;引入黄金正弦策略,不断向最优位置进行搜索,提高了算法的估计精度。将所提算法应用于最大似然DOA估计,实验结果表明:与灰狼优化算法、粒子群算法、正弦余弦算法、光学显微镜算法、蛇鹫优化算法的最大似然DOA估计方法相比,所提算法的最大似然DOA估计具有收敛速度快、估计精度高、运算量小和稳健性更好的性能。
