Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed. INDEX TERMS Artificial bee colony, support vector machine, hyperspectral image, chaotic sequence.

At present, the classification of the hyperspectral image (HSI) based on the deep convolutional network has made great progress. Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. The HDSRN model organically combines 3D CNN, 2D CNN, multiresidual network ROR, and depth-separable convolutions to extract deeper abstract features. On the one hand, due to the addition of multiresidual structures and skip connections, this model can alleviate the problem of over fitting, help the backpropagation of gradients, and extract features more fully. On the other hand, the depth-separable convolutions are used to learn the spatial feature, which reduces the computational cost and alleviates the decline in accuracy. Extensive experiments on the popular HSI benchmark datasets show that the performance of the proposed network is better than that of the existing prevalent methods.

Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network. Keywords: chaotic particle swarm optimization, convergence speed, wavelet neural network IntroductionThe optimism theory and method have existed since ancient time, among which, the relatively representative one is the golden section method. Optimism mainly solves the problem of finding the best solution from many solutions. We can defined optimism as: under certain restrictions, to make the problem reach a best measurement, or to find out a set of parameters, and make certain indicators reach the maximum or minimum. As an important branch of science, the optimism method is gaining more and more attention, and plays important roles in many fields, such as engineering technology, electrical engineering, image processing etc. However, in real life application, since the complexity and nonlinearity of many problems, the target functions of these problems are often discrete and of multi-point value, furthermore, the modeling the problem itself is also very difficult. When applying traditional optimizations like Newton method, dynamic programming, branch and bound method, etc. to solve these complex optimism problems, one usually need to traverse the entire search space, which will waste a lot of time, and can not meet the actual requirement in the aspects of the convergence of the problems and the optimization calculation speed. Therefore, in the current field of optimism, the key job is to seek the efficient optimization.Particle swarm optimization gain the attention of many international scholars in related fields rapidly since its advent. First, Kennedy J and Eberhart R. C. put forward the binary particle swarm optimization in 1997. Then, in 1998, in order to improve the convergence of the algorithm, Shi Y and Eberhart R C introduced the inertia weight parameter into the speed item of the PSO and proposed to dynamically adjust the inertia weight to balance the convergence speed during the process of evolution. This algorithm is called the standard PSO. Then, they put forward the linear decreasing inertia weight LDW-PSO, however, if it deviated from the overall optimum solution in the initial state, then the linear...

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Currently, the fuzzy c-means algorithm plays a certain role in

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