Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises stru...Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.展开更多
Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is importa...Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.展开更多
考虑到传统随机早期检测(Random Early Detection,RED)算法在较强的业务突发度和较大流量抖动的情况下很难获得令人满意的吞吐量这一问题,基于模糊控制理论设计了一个模糊控制器,以提高系统在减少队列长度、降低丢包率中的作用。同时由...考虑到传统随机早期检测(Random Early Detection,RED)算法在较强的业务突发度和较大流量抖动的情况下很难获得令人满意的吞吐量这一问题,基于模糊控制理论设计了一个模糊控制器,以提高系统在减少队列长度、降低丢包率中的作用。同时由于在网络拥塞控制中传统RED算法存在着参数敏感、稳定性差等问题,故在系统中加入一个参数自适应算法,用来稳定队列长度。仿真结果表明该算法在减少队列长度、降低丢包率、提高鲁棒性方面的优化有着明显的效果。展开更多
摘要Optimization is the key to obtaining efficient utilization of resources in structural design.Due to the complex nature of truss systems,this study presents a method based on metaheuristic modelling that minimises structural weight under stress and frequency constraints.Two new algorithms,the Red Kite Optimization Algorithm(ROA)and Secretary Bird Optimization Algorithm(SBOA),are utilized on five benchmark trusses with 10,18,37,72,and 200-bar trusses.Both algorithms are evaluated against benchmarks in the literature.The results indicate that SBOA always reaches a lighter optimal.Designs with reducing structural weight ranging from 0.02%to 0.15%compared to ROA,and up to 6%–8%as compared to conventional algorithms.In addition,SBOA can achieve 15%–20%faster convergence speed and 10%–18%reduction in computational time with a smaller standard deviation over independent runs,which demonstrates its robustness and reliability.It is indicated that the adaptive exploration mechanism of SBOA,especially its Levy flight–based search strategy,can obviously improve optimization performance for low-and high-dimensional trusses.The research has implications in the context of promoting bio-inspired optimization techniques by demonstrating the viability of SBOA,a reliable model for large-scale structural design that provides significant enhancements in performance and convergence behavior.
摘要Hyperspectral(HS)image classification is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,etc.Precise recognition of features from the HS images is important for effective classification outcomes.Additionally,the recent advancements of deep learning(DL)models make it possible in several application areas.In addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics.In this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classification(RDADL-HIC)technique.The proposed RDADL-HIC technique aims to effectively determine the HSI images.In addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimi-zer.Moreover,RDA with gated recurrent unit(GRU)approach is used for the identification and classification of HSIs.The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively.The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures.The comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.
摘要考虑到传统随机早期检测(Random Early Detection,RED)算法在较强的业务突发度和较大流量抖动的情况下很难获得令人满意的吞吐量这一问题,基于模糊控制理论设计了一个模糊控制器,以提高系统在减少队列长度、降低丢包率中的作用。同时由于在网络拥塞控制中传统RED算法存在着参数敏感、稳定性差等问题,故在系统中加入一个参数自适应算法,用来稳定队列长度。仿真结果表明该算法在减少队列长度、降低丢包率、提高鲁棒性方面的优化有着明显的效果。