In this paper, the problem of locally optimum detection of weak pulse signals in narrow-band non-Gaussian noise is discussed. A generalized model is proposed for locally optimum detectors (LOD) and the corresponding p...In this paper, the problem of locally optimum detection of weak pulse signals in narrow-band non-Gaussian noise is discussed. A generalized model is proposed for locally optimum detectors (LOD) and the corresponding physical meaning is explained. On the basis of this generalized model, the LOD structures are derived for detecting both coherent- and incoherent-pulse signals in narrow-band non-Gaussian noise. The asymptotic relative efficiency (ARE) due to Pitman is used to evaluate the performance of these LODs. Finally, numerical calculations are carried out for the AREs of these LODs and some valuable results are obtained.展开更多
A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process...A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance (RC) networks identification problem, and improvements of the result are obtained by using this algorithm.展开更多
In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local ...In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.展开更多
The definition of local optimum solution of the discrete optimization is first given.and then a comprehensive combinatorial algorithm is proposed in this paper. Two-leveloptimum method is used in the algorithm. In t...The definition of local optimum solution of the discrete optimization is first given.and then a comprehensive combinatorial algorithm is proposed in this paper. Two-leveloptimum method is used in the algorithm. In the first level optimization, anapproximate local optimum solution X is found by using the heuristic algorithm,relative difference quotient algorithm. with high computational efficiency and highperformance demonstrated by the performance test of random samples. In the secondlevel, a mathematical model of (- 1, 0, 1) programming is established first, and then itis changed into (0, 1) programming model. The local optimum solution X will befrom the (0. 1) programming by using the delimitative and combinatorial algorithm orthe relative difference quotient algorithm. By this algorithm, the local optimumsolution can be obtained certainly, and a method is provnded to judge whether or notthe approximate optimum solution obtained by heuristic algorithm is an optimumsolution. The above comprehensive combinatorial algorithm has higher computationalefficiency.展开更多
Harris hawks optimization(HHO)algorithm is an efficient method of solving function optimization problems.However,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnat...Harris hawks optimization(HHO)algorithm is an efficient method of solving function optimization problems.However,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnation to local optimum.To this end,an improved HHO(IHHO)algorithm based on good point set and nonlinear convergence formula is proposed.First,a good point set is used to initialize the positions of the population uniformly and randomly in the whole search area.Second,a nonlinear exponential convergence formula is designed to balance exploration stage and exploitation stage of IHHO algorithm,aiming to find all the areas containing the solutions more comprehensively and accurately.The proposed IHHO algorithm tests 17 functions and uses Wilcoxon test to verify the effectiveness.The results indicate that IHHO algorithm not only has faster convergence speed than other comparative algorithms,but also improves the accuracy of solution effectively and enhances its robustness under low dimensional and high dimensional conditions.展开更多
During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the r...During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.展开更多
文摘In this paper, the problem of locally optimum detection of weak pulse signals in narrow-band non-Gaussian noise is discussed. A generalized model is proposed for locally optimum detectors (LOD) and the corresponding physical meaning is explained. On the basis of this generalized model, the LOD structures are derived for detecting both coherent- and incoherent-pulse signals in narrow-band non-Gaussian noise. The asymptotic relative efficiency (ARE) due to Pitman is used to evaluate the performance of these LODs. Finally, numerical calculations are carried out for the AREs of these LODs and some valuable results are obtained.
基金supported by the National Basic Research Program of China(2011CB013103)
文摘A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance (RC) networks identification problem, and improvements of the result are obtained by using this algorithm.
文摘In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.
文摘The definition of local optimum solution of the discrete optimization is first given.and then a comprehensive combinatorial algorithm is proposed in this paper. Two-leveloptimum method is used in the algorithm. In the first level optimization, anapproximate local optimum solution X is found by using the heuristic algorithm,relative difference quotient algorithm. with high computational efficiency and highperformance demonstrated by the performance test of random samples. In the secondlevel, a mathematical model of (- 1, 0, 1) programming is established first, and then itis changed into (0, 1) programming model. The local optimum solution X will befrom the (0. 1) programming by using the delimitative and combinatorial algorithm orthe relative difference quotient algorithm. By this algorithm, the local optimumsolution can be obtained certainly, and a method is provnded to judge whether or notthe approximate optimum solution obtained by heuristic algorithm is an optimumsolution. The above comprehensive combinatorial algorithm has higher computationalefficiency.
基金supported by the National Natural Science Foundation of China(61872126)。
文摘Harris hawks optimization(HHO)algorithm is an efficient method of solving function optimization problems.However,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnation to local optimum.To this end,an improved HHO(IHHO)algorithm based on good point set and nonlinear convergence formula is proposed.First,a good point set is used to initialize the positions of the population uniformly and randomly in the whole search area.Second,a nonlinear exponential convergence formula is designed to balance exploration stage and exploitation stage of IHHO algorithm,aiming to find all the areas containing the solutions more comprehensively and accurately.The proposed IHHO algorithm tests 17 functions and uses Wilcoxon test to verify the effectiveness.The results indicate that IHHO algorithm not only has faster convergence speed than other comparative algorithms,but also improves the accuracy of solution effectively and enhances its robustness under low dimensional and high dimensional conditions.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2022A1515011313)in part by Guangdong Innovative and Entrepreneurial Research Team Program(Grant No.2019ZT08Z780)in part by Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents。
文摘During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.