A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A mult...A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.展开更多
This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optima...This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.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
基金Fujian Province Youth Foundation for InnovativResearch Under Grant No. 2006F3008Fujian ProvincEducational Special Foundation Under Grant No. JA06027
文摘A semi-active strategy for model predictive control （MPC）, in which magneto-rheological dampers are used as an actuator, is presented for use in reducing the nonlinear seismic response of high-rise buildings. A multi-step predictive model is developed to estimate the seismic performance of high-rise buildings, taking into account of the effects of nonlinearity, time-variability, model mismatching, and disturbances and uncertainty of controlled system parameters by the predicted error feedback in the multi-step predictive model. Based on the predictive model, a Kalman-Bucy observer suitable for semi-active strategy is proposed to estimate the state vector from the acceleration and semi-active control force feedback. The main advantage of the proposed strategy is its inherent stability, simplicity, on-line real-time operation, and the ability to handle nonlinearity, uncertainty, and time-variability properties of structures. Numerical simulation of the nonlinear seismic responses of a controlled 20-story benchmark building is carried out, and the simulation results are compared to those of other control systems. The results show that the developed semi-active strategy can efficiently reduce the nonlinear seismic response of high-rise buildings.
基金King Saud University for funding this research through Researchers Supporting Program Number(RSPD2023R704),King Saud University,Riyadh,Saudi Arabia.
文摘This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.
文摘蜣螂优化器(dung beetle optimizer,DBO)是一种有效的元启发式算法。蜣螂优化算法虽然具有寻优能力强,收敛速度快的特点,但同时也存在全局探索和局部开发能力不平衡,容易陷入局部最优,且全局探索能力较弱的缺点。提出了一种改进的DBO算法来解决全局优化问题,命名为MSADBO。受改进正弦算法(improved sine algorithm,MSA)的启发,赋予蜣螂MSA的全局探索和局部开发能力,扩大其搜索范围,提高全局探索能力,减少陷入局部最优的可能性。同时加入了混沌映射初始化和变异算子进行扰动。为了验证MSADBO的有效性,对该算法采用23个基准测试函数进行了测试,并与其他知名的元启发式算法进行了比较。结果表明,该算法具有良好的性能。为了进一步阐述MSADBO算法的实际应用潜力,将该算法成功地应用于3个工程设计问题。实验结果表明,所提出的MSADBO算法可以有效地处理实际应用问题。
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.
