A novel global harmony search (NGHS) algorithm, as proposed in 2010, is an improved algorithm that combines the harmony search (HS), particle swarm optimization (PSO), and a genetic algorithm (GA). Moreover, the fixed parameter of mutation probability was used in the NGHS algorithm. However, appropriate parameters can enhance the searching ability of a metaheuristic algorithm, and their importance has been described in many studies. Inspired by the adjustment strategy of the improved harmony search (IHS) algorithm, a dynamic adjusting novel global harmony search (DANGHS) algorithm, which combines NGHS and dynamic adjustment strategies for genetic mutation probability, is introduced in this paper. Moreover, extensive computational experiments and comparisons are carried out for 14 benchmark continuous optimization problems. The results show that the proposed DANGHS algorithm has better performance in comparison with other HS algorithms in most problems. In addition, the proposed algorithm is more efficient than previous methods. Finally, different strategies are suitable for different situations. Among these strategies, the most interesting and exciting strategy is the periodic dynamic adjustment strategy. For a specific problem, the periodic dynamic adjustment strategy could have better performance in comparison with other decreasing or increasing strategies. These results inspire us to further investigate this kind of periodic dynamic adjustment strategy in future experiments.

Cost control is becoming increasingly important in hospital management. Hospital operating rooms have high resource consumption because they are a major part of a hospital. Thus, the optimal use of operating rooms can lead to high resource savings. However, because of the uncertainty of the operation procedures, it is difficult to arrange for the use of operating rooms in advance. In general, the durations of both surgery and anesthesia emergence determine the time requirements of operating rooms, and these durations are difficult to predict. In this study, we used an artificial neural network to construct a surgery and anesthesia emergence duration-prediction system. We propose an intelligent data preprocessing algorithm to balance and enhance the training dataset automatically. The experimental results indicate that the prediction accuracies of the proposed serial prediction systems are acceptable in comparison to separate systems.

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.

Commutation is a judicial policy that is implemented in most countries. The recidivism rate of commuted prisoners directly affects people’s perceptions and trust of commutation. Hence, if the recidivism rate of a commuted prisoner could be accurately predicted before the person returns to society, the number of reoffences could be reduced; thereby, enhancing trust in the process. Therefore, it is of considerable importance that the recidivism rates of commuted prisoners are accurately predicted. The dynamic adjusting novel global harmony search (DANGHS) algorithm, as proposed in 2018, is an improved algorithm that combines dynamic parameter adjustment strategies and the novel global harmony search (NGHS). The DANGHS algorithm improves the searching ability of the NGHS algorithm by using dynamic adjustment strategies for genetic mutation probability. In this paper, we combined the DANGHS algorithm and an artificial neural network (ANN) into a DANGHS-ANN forecasting system to predict the recidivism rate of commuted prisoners. To verify the prediction performance of the DANGHS-ANN algorithm, we compared the experimental results with five other forecasting systems. The results showed that the proposed DANGHS-ANN algorithm gave more accurate predictions. In addition, the use of the threshold linear posterior decreasing strategy with the DANGHS-ANN forecasting system resulted in more accurate predictions of recidivism. Finally, the metaheuristic algorithm performs better searches with the dynamic parameter adjustment strategy than without it.

Silicon wafer is the most crucial material in the semiconductor manufacturing industry. Owing to limited resources, the reclamation of monitor and dummy wafers for reuse can dramatically lower the cost, and become a competitive edge in this industry. However, defects such as void, scratches, particles, and contamination are found on the surfaces of the reclaimed wafers. Most of the reclaimed wafers with the asymmetric distribution of the defects, known as the “good (G)” reclaimed wafers, can be re-polished if their defects are not irreversible and if their thicknesses are sufficient for re-polishing. Currently, the “no good (NG)” reclaimed wafers must be first screened by experienced human inspectors to determine their re-usability through defect mapping. This screening task is tedious, time-consuming, and unreliable. This study presents a deep-learning-based reclaimed wafers defect classification approach. Three neural networks, multilayer perceptron (MLP), convolutional neural network (CNN) and Residual Network (ResNet), are adopted and compared for classification. These networks analyze the pattern of defect mapping and determine not only the reclaimed wafers are suitable for re-polishing but also where the defect categories belong. The open source TensorFlow library was used to train the MLP, CNN, and ResNet networks using collected wafer images as input data. Based on the experimental results, we found that the system applying CNN networks with a proper design of kernels and structures gave fast and superior performance in identifying defective wafers owing to its deep learning capability, and the ResNet averagely exhibited excellent accuracy, while the large-scale MLP networks also acquired good results with proper network structures.