The paper analyses the significance of reactive power optimization,generalizes the current situation of power system *** optimizationalgorithms were introduced in this paper such as traditional optimizationalgorithm...
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The paper analyses the significance of reactive power optimization,generalizes the current situation of power system *** optimizationalgorithms were introduced in this paper such as traditional optimizationalgorithm,intelligence optimization algorithm,including the methods of linear programming,Newton's method,heuristic optimizationalgorithm,*** research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous *** purpose of the research is to find the optimal solution of reactive power optimizationalgorithm,minimize the transport network loss of power system,and improve the quality of users.
This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel....
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This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine-grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU-based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to163.09xcompared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.
In view of slow convergence speed, large steady mean square error(MSE), and existing blind phase for the constant modulus blind equalization algorithm(CMA), a multi-modulus blind equalization algorithm based on memeti...
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ISBN:
(纸本)9781510812055
In view of slow convergence speed, large steady mean square error(MSE), and existing blind phase for the constant modulus blind equalization algorithm(CMA), a multi-modulus blind equalization algorithm based on memetic algorithm(MA-MMA) is proposed, which combines the basic idea of intelligent optimizationalgorithm and introduces the individual own evolution and social behavior among individuals to the blind equalization technology. In this proposed algorithm, the reciprocal of the cost function of multi-modulus blind equalization algorithm(MMA) is defined as the fitness function of the memetic algorithm(MA), the initial optimal weight vector of the MMA is optimized by using the global information sharing mechanism and local depth search ability of the MA. When the initial optimum weight vector of the MMA is obtained, the weight vector of the MMA may be updated. The simulation results with the higher-order APSK multi-modulus signals show that, compared with the CMA, the MMA, and the multi-modulus blind equalization algorithm based on genetic algorithm(GA-MMA), the proposed MA-MMA has the fastest convergence speed, smallest mean square error(MSE), and clearest output constellations.
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