Search

Attention mechanism, which is a cognitive process of selectively concentrating on certain information while ignoring others, has been successfully employed in deep learning. In this paper, we introduce the attention mechanism into a particle swarm optimizer and propose an attention-based particle swarm optimizer (APSO) for large scale optimization. In the proposed method, the attention mechanism is introduced such that activating different particles to participate in evolution at different stages of evolution. Further, an attention-based particle learning is devised to randomly select three particles from a predominant sub-swarm, which is activated by the attention mechanism, to guide the learning of particles. The cooperation of these two strategies could be employed to achieve a balanced evolution search, thus appropriately searching the space of large-scale optimization problems. Extensive experiments have been carried out on CEC'2010 and CEC'2013 large scale optimization benchmark functions to evaluate the performance of proposed method and to compare with related methods. The results show the superiority of proposed method. [ABSTRACT FROM AUTHOR]

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel $N$ -state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions. [ABSTRACT FROM AUTHOR]

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of traditional clustering approaches with applications in analyzing real-time patient attendance data from an accident & emergency (A&E) department in a local U.K. hospital. In the proposed randomly occurring distributedly delayed PSO (RODDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on whether the velocity updating model switches from one mode to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, randomly occurring time-delays that reflect the history of previous personal best and global best particles are introduced in the velocity updating model in a distributed manner. Eight well-known benchmark functions are employed to evaluate the proposed RODDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the RODDPSO algorithm is successfully exploited in the patient clustering problem for data analysis with respect to a local A&E department in West London. Experiment results demonstrate that the RODDPSO-based clustering method is superior over two other well-known clustering algorithms. [ABSTRACT FROM AUTHOR]

In wire arc additive manufacturing (WAAM), the electric arc is an essential part of the welding equipment, which serves as the heat source and is directed by the current and voltage. The working status of the electric arc is an important factor in determining the quality of the fabricated components. During the welding process, the current and voltage may change abruptly due to some abnormalities in the operating conditions, which may affect the working status and thereby affect the quality of products. Such abnormal changes in the current and voltage can be treated as outliers. In order to identify outliers in current and voltage to further improve the welding process, in this paper, a novel deep-transfer-learning-embedded outlier detection approach is developed for WAAM. A new domain adaptation strategy is designed where the cross-domain discrepancies of the marginal distribution and conditional distribution are minimized. Specifically, two separate coefficients are introduced to adjust the conditional domain discrepancies of normal instances and outliers with the purpose of alleviating the data imbalance problem. The particle swarm optimizer is employed to adjust the hyper-parameters. The developed deep transfer learning framework is exploited in designing a new outlier detector with application to WAAM. The proposed approach is exploited in real-world industrial data collected through the WAAM process. Experimental results demonstrate that the proposed outlier detection approach outperforms the standard deep-learning-based outlier detector approach and the standard transfer-learning-embedded outlier detection approach in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a modified collaborative filtering (MCF) algorithm with improved performance is developed for recommendation systems with application in predicting baseline data of Friedreich's Ataxia (FRDA) patients. The proposed MCF algorithm combines the individual merits of both the user-based collaborative filtering (UBCF) method and the item-based collaborative filtering (IBCF) method, where both the positively and negatively correlated neighbors are taken into account. The weighting parameters are introduced to quantify the degrees of utilizations of the UBCF and IBCF methods in the rating prediction, and the particle swarm optimization algorithm is applied to optimize the weighting parameters in order to achieve an adequate tradeoff between the positively and negatively correlated neighbors in terms of predicting the rating values. To demonstrate the prediction performance of the proposed MCF algorithm, the developed MCF algorithm is employed to assist with the baseline data collection for the FRDA patients. The effectiveness of the proposed MCF algorithm is confirmed by extensive experiments and, furthermore, it is shown that our algorithm outperforms some conventional approaches. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm. [ABSTRACT FROM AUTHOR]

Large-scale optimization, which has received much attention in recent years, is inherently a challenging problem. This paper proposes a particle swarm optimizer with multi-level population sampling and dynamic p -learning mechanisms to address the problem. The multi-level sampling mechanism in the proposed method is developed for supporting a balanced evolutionary search. The mechanism works by partitioning the particles of swarm into multi-levels based on their fitness before each generation of evolution. A subset of swarm is then dynamically sampled from the particles at various levels for evolution such that encouraging exploration at the beginning of evolution while exploitation towards the end of evolution, thus appropriately searching the space. The dynamic p -learning mechanism, on the other hand, is introduced to allow efficient particle learning while preserving the swarm diversity during evolution. In this mechanism, each particle is devised to learn from one of the top 100 p% particles of the sub-swarm and the value of p associated with each particle is dynamically adjusted during evolution. By employing the above two mechanisms, the resulting method aims to appropriate search the solution space of large-scale global optimization problem for identifying the optimal or near-optimal solution. The performance of the proposed method has been evaluated on CEC'2010 and CEC'2013 benchmark suites for large-scale optimization and compared with related methods. Our results confirm the merits of the devised mechanisms in the proposed method. The results also show that our method can achieve a superior performance and outperform related methods. [ABSTRACT FROM AUTHOR]

Aiming at limitations in fully exploiting the temporal correlation features of the original signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training data under multiple working conditions, this paper proposes an optimized Convolutional Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) scheme for bearing fault diagnosis under multiple working conditions with limited training samples. A CNN-BiLSTM network is developed to obtain precise fault features and high detection accuracy by extracting high-dimensional and temporal correlation features of raw vibration signals. An improved particle swarm optimization (PSO) algorithm is leveraged to optimize the training hyperparameters of the CNN-BiLSTM network for further advances in fault diagnosis performance. The optimized CNN-BiLSTM network is regarded as a pre-trained model and transferred to new working conditions to achieve satisfactory fault diagnosis results based on limited training samples. Several comprehensive experiments are implemented to confirm the excellent performance of the proposed schemes, especially efficiently addressing the challenges of model training and fault diagnosis in new working conditions with scarce samples. [ABSTRACT FROM AUTHOR]

Abstract In healthcare sector, it is of crucial importance to accurately diagnose Alzheimer's disease (AD) and its prophase called mild cognitive impairment (MCI) so as to prevent degeneration and provide early treatment for AD patients. In this paper, a framework is proposed for the diagnosis of AD, which consists of MRI images preprocessing, feature extraction, principal component analysis, and the support vector machine (SVM) model. In particular, a new switching delayed particle swarm optimization (SDPSO) algorithm is proposed to optimize the SVM parameters. The developed framework based on the SDPSO-SVM model is successfully applied to the classification of AD and MCI using MRI scans from ADNI dataset. Our developed algorithm can achieve excellent classification accuracies for 6 typical cases. Furthermore, experiment results demonstrate that the proposed algorithm outperforms several SVM models and also two other state-of-art methods with deep learning embedded, thereby serving as an effective AD diagnosis method. [ABSTRACT FROM AUTHOR]