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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.

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Body bias control is an efficient means of balancing the trade-off between leakage power and performance especially for chips with silicon on thin buried oxide (SOTB), a type of FD-SOI technology. In this work, a method for finding the optimal combination of the supply voltage and body bias voltage to the core and memory is proposed and applied to a real micro-controller chip using SOTB CMOS technology. By obtaining several coefficients of equations for leakage power, switching power and operational frequency from the real chip measurements, the optimized voltage setting can be obtained for the target operational frequency. The power consumption lost by the error of optimization is 12.6% at maximum, and it can save at most 73.1% of power from the cases where only the body bias voltage is optimized. This method can be applied to the latest FD-SOI technologies.

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.