A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-wor...A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-word-length FIR filter design problem was converted into optimization of the filter coefficients, An optimization method combined with local discrete random search and simulated annealing was proposed, with the result of optimum solution in the sense of Chebyshev approximation. The proposed method can simplify the design process of FIR filter and reduce the calculation burden. The simulation result indicates that the proposed method is superior to the traditional round off method and can reduce the value of the objective function to 41%～74%.展开更多
With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execu...With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.展开更多
文摘A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-word-length FIR filter design problem was converted into optimization of the filter coefficients, An optimization method combined with local discrete random search and simulated annealing was proposed, with the result of optimum solution in the sense of Chebyshev approximation. The proposed method can simplify the design process of FIR filter and reduce the calculation burden. The simulation result indicates that the proposed method is superior to the traditional round off method and can reduce the value of the objective function to 41%～74%.
基金Foundation items:National Natural Science Foundation of China(No.62303108)Fundamental Research Funds for the Central Universities,China(No.CUSF-DH-T-2023065)。
文摘With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.
