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A microarray can be easily used for quantitatively analyzing the expression levels of DNA genes. Yet, the noises introduced during the application will greatly affect the accuracy of DNA sequence detection. How to reduce the noise constitutes a challenging problem in microarray analysis. Especially, due to the weak fluorescence response, the image of microarray contains difficulties of the low peak-signal-tonoise ratio (PSNR) and luminance contrast. To solve the problem that the wavelet threshold denoising method has poor effective on low PSNR image, a wavelet denoising approach based on compression sensing (CS) optimized by the neural dynamics optimization algorithm (NDOA) is proposed, which preferably solves the denoising difficulties of noise pollution in the microarray image. Under the condition of Gaussian random observation matrix, the effectiveness of NDOA-optimized wavelet denoising based on CS gets better work than the orthogonal matching pursuit and its improved algorithms. The experimental results indicate that the expected wavelet coefficients of the noiseless image have been reconstructed with higher quality. When the compression sampling rate for microarray image is 0.875, the PSNR of the NDOA-optimized wavelet denoising algorithm based on CS is increased about 9 dB, and the root mean squared error is reduced obviously too, in comparison with the wavelet soft-threshold denoising method. It shows that the NDOA-optimized method improves the performance of the classical wavelet threshold denoising. INDEX TERMS Compressed sensing, wavelet denoising, DNA microarray, image filtering, NDOA.

As one of the great advances in modern technology, the microarray is widely used in many fields, including biomedical research, clinical diagnosis, and so on. Evidently, in order to extract the intensity of fluorescence bio-probes accurately, we need to pay special attention to the gridding of microarray at first. To solve the poor effect of the traditional Otsu method for microarray gridding, an innovative algorithm of Otsu optimized by multilevel thresholds is proposed to improve the accuracy and effectiveness of the microarray image gridding and segmentation. The experimental results indicate that considering the physical information carried by microarrays, the improved algorithm of Otsu optimized by multilevel thresholds achieves high-quality gridding and establishes the bio-spot coordinates more precisely. Compared with the traditional Otsu method, its gridding error is reduced to zero, and the integrated relative error of bio-spot coordinates is decreased from 2.89% to 1.05%. This optimization of Otsu combined with physical information of spot-matrix will greatly improve the performance of segmentation so as to make the contribution to extracting the fluorescence intensity of microarray accurately. INDEX TERMS Microarray image, Otsu method, multilevel thresholds, gridding, physical information.

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Marine Predator Algorithm (MPA) is an optimization algorithm inspired by the behavior of predator and prey to catch their own food. MPA is simple and easy to implement. To further improve the performance of MPA, this paper proposes a Multigroup Marine Predator Algorithm (MGMPA). The multigroup mechanism is to divide the initial population into several independent groups. These groups generate the top predator and the Elite matrix based on different strategies and share information after a fixed iteration. Above strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. To verify its performance, the paper compares MGMPA with some classic algorithms such as Particle Swarm Optimization (PSO), Parallel Particle Swarm Optimization (PPSO), Slap Swarm Algorithm (SSA), and Marine Predator Algorithm (MPA). In addition, the proposed MGMPA is also applied to solve Economic Load Dispatch problem (ELD). The experimental results show that the proposed MGMPA has significant advantages under the CEC2013 suite and obtains the minimum cost of power system operation and the maximum economic benefits in the application.