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This paper introduces the relative principium of K-Means algorithm, simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm at first. Then, in allusion to the influence of the initial value of the K-Means algorithm on the optimal solution of the algorithm, a hybrid algorithm of K-Means based on SA-PSO is proposed. The new algorithm uses the advantage of jumping out of local...
Artificial fish swarm algorithm (AFSA) is a newly proposed swarm intelligent optimization algorithm. It is proved to be a promising approach to complex engineering problems, yet still there exist some defects of this algorithm. To solve the problem that AFSA has a low rate of convenience, low optimization precision, premature convergence and poor ability of balancing exploitation and exploration,...
Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported...
In order to improve the searching speed and the quality of global optimal solution, we propose an improved algorithm based on Artificial Bee Colony(ABC) algorithm, which can search the space by stochastic optimization and dynamic regulation (named SRABC). Firstly, the improved algorithm can update the next location of ABC algorithm, which can perfect the correlation for the bee colony. Secondly, we...
To deal with time-varying equations, a novel recurrent neural network, named varying-parameter convergentdifferential neural network (in short, VP-CDNN), is proposed, modeled and analyzed. It is designed by a matrix-valued error function and the design parameter is time-varying, which enables the VP-CDNN to have good convergence and robustness. For illustration and comparison, a scalar-valued error...
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