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There are two common challenges in particle swarm optimization (PSO) research, that is, selecting proper exemplars and designing an efficient learning model for a particle. In this article, we propose a triple archives PSO (TAPSO), in which particles in three archives are used to deal with the above two challenges. First, particles who have better fitness (i.e., elites) are recorded in one archive while other particles who offer faster progress, called profiteers in this article, are saved in another archive. Second, when breeding each dimension of a potential exemplar for a particle, we choose a pair of elite and profiteer from corresponding archives as two parents to generate the dimension value by ordinary genetic operators. Third, each particle carries out a specific learning model according to the fitness of its potential exemplars. Furthermore, there is no acceleration coefficient in TAPSO aiming to simplify the learning models. Finally, if an exemplar has excellent performance, it will be regarded as an outstanding exemplar and saved in the third archive, which can be reused by inferior particles aiming to enhance the exploitation and to save computing resources. The experimental results and comparisons between TAPSO and other eight PSOs on 30 benchmark functions and four real applications suggest that TAPSO attains very promising performance in different types of functions, contributing to both higher solution accuracy and faster convergence speed. Furthermore, the effectiveness and efficiency of these new proposed strategies are discussed based on extensive experiments.

Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

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Feature selection is an important pre-processing step in machine learning and data mining tasks, which improves the performance of the learning models by removing redundant and irrelevant features. Many feature selection algorithms have been widely studied, including greedy and random search approaches, to find a subset of the most important features for fulfilling a particular task (i.e., classification and regression). As a powerful swarm-based meta-heuristic method, particle swarm optimization (PSO) is reported to be suitable for optimization problems with continuous search space. However, the traditional PSO has rarely been applied to feature selection as a discrete space search problem. In this paper, a novel feature selection algorithm based on PSO with learning memory (PSO-LM) is proposed. The goal of the learning memory strategy is designed to inherit much more useful knowledge from those individuals who have higher fitness and offer faster progress, and the genetic operation is used to balance the local exploitation and the global exploration of the algorithm. Moreover, the k-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. The proposed method has been evaluated on some international standard data sets, and the results demonstrated its superiority compared with those wrapper-based feature selection methods.

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