Understanding physiological responses and drought adaptation strategies of woody plant leaf traits in sub-humid to semi-arid regions is of vital importance to understand the interplay between ecological processes and ...Understanding physiological responses and drought adaptation strategies of woody plant leaf traits in sub-humid to semi-arid regions is of vital importance to understand the interplay between ecological processes and plant resource-allocation strategies of different tree species.Seasonal variations of leaf morphological traits,stoichiometric traits and their relationships of two drought tolerant woody species,live oak(Quercus virginiana)and honey mesquite(Prosopis glandulosa)and two less drought tolerant species,sugarberry(Celtis laevigata)and white ash(Fraxinus americana)were analyzed in a sub-humid to semi-arid area of south Texas,USA.Our findings demonstrate that for the two drought tolerant species,the leguminous P.glandulosa had the highest specific leaf area,leaf N,P,and lowest leaf area and dry mass,indicating that P.glandulosa adapts to an arid habitat by decreasing leaf area,thus reducing water loss,reflecting a resource acquisition strategy.While the evergreen species Q.virginiana exhibited higher leaf dry mass,leaf dry matter content,C content,C:N,C:P and N:P ratios,adapts to an arid habitat through increased leaf thickness and thus reduced water loss,reflecting a resource conservation strategy in south Texas.For the two less drought tolerant deciduous species,the variations of leaf traits in C.laevigata and F.americana varied between Q.virginiana and P.glandulosa,reflecting a trade-off between rapid plant growth and nutrient maintenance in a semi-arid environment.展开更多
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
A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm...A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.展开更多
基金funded by the China Scholarship Council(CSC)a research award from Texas A&M University-Kingsville+1 种基金the Natural Science Foundation of Anhui Province(Grant Number 1408085QC57)Youth Science Fund of Anhui Agricultural University(Grant Number 2012zd015)
文摘Understanding physiological responses and drought adaptation strategies of woody plant leaf traits in sub-humid to semi-arid regions is of vital importance to understand the interplay between ecological processes and plant resource-allocation strategies of different tree species.Seasonal variations of leaf morphological traits,stoichiometric traits and their relationships of two drought tolerant woody species,live oak(Quercus virginiana)and honey mesquite(Prosopis glandulosa)and two less drought tolerant species,sugarberry(Celtis laevigata)and white ash(Fraxinus americana)were analyzed in a sub-humid to semi-arid area of south Texas,USA.Our findings demonstrate that for the two drought tolerant species,the leguminous P.glandulosa had the highest specific leaf area,leaf N,P,and lowest leaf area and dry mass,indicating that P.glandulosa adapts to an arid habitat by decreasing leaf area,thus reducing water loss,reflecting a resource acquisition strategy.While the evergreen species Q.virginiana exhibited higher leaf dry mass,leaf dry matter content,C content,C:N,C:P and N:P ratios,adapts to an arid habitat through increased leaf thickness and thus reduced water loss,reflecting a resource conservation strategy in south Texas.For the two less drought tolerant deciduous species,the variations of leaf traits in C.laevigata and F.americana varied between Q.virginiana and P.glandulosa,reflecting a trade-off between rapid plant growth and nutrient maintenance in a semi-arid environment.
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A hybrid strategy is proposed to solve the problems of poor population diversity, insufficient convergence accuracy and susceptibility to local optimal values in the original Arctic Puffin Optimization (APO) algorithm, Enhanced Tangent Flight Adaptive Arctic Puffin Optimization with Elite initialization and Adaptive t-distribution Mutation (ETAAPO). Elite initialization improves initial population quality and accelerates convergence. Tangent Flight of the Tangent search algorithm replaces Levy Flight to balance local search and global exploration. The adaptive t-distribution mutation strategy enhances the optimization ability. ETAAPO was tested on CEC2021 functions, Wilcoxon rank-sum tests, and engineering problems, demonstrating superior optimization performance and faster convergence.
